Method and system for detecting abnormal operation in a hydrocracker

ABSTRACT

A system and method for detecting abnormal operation of a hydrocracker includes a hydrocracker model. The model may be configurable to include one or more regression models corresponding to different operating regions of the portion of the hydrocracker. The system and method may also determine if a monitored temperature difference variable deviates significantly from the temperature difference variable predicted by the model. If there is a significant deviation, this may indicate an abnormal operation.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims priority from U.S. Provisional Application Ser.No. 60/847,785 which was filed on Sep. 28, 2006, entitled “ABNORMALSITUATION PREVENTION IN A HYDROCRACKER.” The above-referencedprovisional patent application is hereby incorporated by referenceherein, in its entirety.

TECHNICAL FIELD

This disclosure relates generally to process control systems and, moreparticularly, to systems for monitoring and/or modeling hydrocrackers.

DESCRIPTION OF THE RELATED ART

Process control systems, such as distributed or scalable process controlsystems like those used in chemical, petroleum or other processes,typically include one or more process controllers communicativelycoupled to each other, to at least one host or operator workstation andto one or more field devices via analog, digital or combinedanalog/digital buses. The field devices, which may be, for examplevalves, valve positioners, switches and transmitters (e.g., temperature,pressure and flow rate sensors), perform functions within the processsuch as opening or closing valves and measuring process parameters. Theprocess controller receives signals indicative of process measurementsmade by the field devices and/or other of information pertaining to thefield devices, uses this information to implement a control routine andthen generates control signals which are sent over the buses to thefield devices to control the operation of the process. Information fromthe field devices and the controller is typically made available to oneor more applications executed by the operator workstation to enable anoperator to perform any desired function with respect to the process,such as viewing the current state of the process, modifying theoperation of the process, etc.

In the past, conventional field devices were used to send and receiveanalog (e.g., 4 to 20 milliamps) signals to and from the processcontroller via an analog bus or analog lines. These 4 to 20 mA signalswere limited in nature in that they were indicative of measurements madeby the device or of control signals generated by the controller requiredto control the operation of the device. However, in the past decade orso, smart field devices including a microprocessor and a memory havebecome prevalent in the process control industry. In addition toperforming a primary function within the process, smart field devicesstore data pertaining to the device, communicate with the controllerand/or other devices in a digital or combined digital and analog format,and perform secondary tasks such as self calibration, identification,diagnostics, etc. A number of standard and open smart devicecommunication protocols such as the HART®, PROFIBUS®, WORLDFIP®, DeviceNet®, and CAN protocols, have been developed to enable smart fielddevices made by different manufacturers to be used together within thesame process control network. Moreover, the all digital, two wire busprotocol promulgated by the Fieldbus Foundation, known as theFOUNDATION™ Fieldbus (hereinafter “Fieldbus”) protocol uses functionblocks located in different field devices to perform control operationspreviously performed within a centralized controller. In this case, theFieldbus field devices are capable of storing and executing one or morefunction blocks, each of which receives inputs from and/or providesoutputs to other function blocks (either within the same device orwithin different devices), and performs some process control operation,such as measuring or detecting a process parameter, controlling a deviceor performing a control operation, like implementing aproportional-integral-derivative (PID) control routine. The differentfunction blocks within a process control system are configured tocommunicate with each other (e.g., over a bus) to form one or moreprocess control loops, the individual operations of which are spreadthroughout the process and are, thus, decentralized.

Information from the field devices and the process controllers istypically made available to one or more other hardware devices such asoperator workstations, maintenance workstations, personal computers,handheld devices, data historians, report generators, centralizeddatabases, etc., to enable an operator or a maintenance person toperform desired functions with respect to the process such as, forexample, changing settings of the process control routine, modifying theoperation of the control modules within the process controllers or thesmart field devices, viewing the current state of the process or ofparticular devices within the process plant, viewing alarms generated byfield devices and process controllers, simulating the operation of theprocess for the purpose of training personnel or testing the processcontrol software, diagnosing problems or hardware failures within theprocess plant, etc.

While a typical process plant has many process control andinstrumentation devices such as valves, transmitters, sensors, etc.connected to one or more process controllers, there are many othersupporting devices that are also necessary for or related to processoperation. These additional devices include, for example, power supplyequipment, power generation and distribution equipment, rotatingequipment such as turbines, motors, etc., which are located at numerousplaces in a typical plant. While this additional equipment does notnecessarily create or use process variables and, in many instances, isnot controlled or even coupled to a process controller for the purposeof affecting the process operation, this equipment is neverthelessimportant to, and ultimately necessary for proper operation of theprocess.

As is known, problems frequently arise within a process plantenvironment, especially a process plant having a large number of fielddevices and supporting equipment. These problems may take the form ofbroken or malfunctioning devices, logic elements, such as softwareroutines, being in improper modes, process control loops beingimproperly tuned, one or more failures in communications between deviceswithin the process plant, etc. These and other problems, while numerousin nature, generally result in the process operating in an abnormalstate (i.e., the process plant being in an abnormal situation) which isusually associated with suboptimal performance of the process plant.Many diagnostic tools and applications have been developed to detect anddetermine the cause of problems within a process plant and to assist anoperator or a maintenance person to diagnose and correct the problems,once the problems have occurred and been detected. For example, operatorworkstations, which are typically connected to the process controllersthrough communication connections such as a direct or wireless bus,Ethernet, modem, phone line, and the like, have processors and memoriesthat are adapted to run software or firmware, such as the DeltaV™ andOvation control systems, sold by Emerson Process Management whichincludes numerous control module and control loop diagnostic tools.Likewise, maintenance workstations, which may be connected to theprocess control devices, such as field devices, via the samecommunication connections as the controller applications, or viadifferent communication connections, such as Object Linking andEmbedding (OLE) for Process Control (OPC) connections, handheldconnections, etc., typically include one or more applications designedto view maintenance alarms and alerts generated by field devices withinthe process plant, to test devices within the process plant and toperform maintenance activities on the field devices and other deviceswithin the process plant. Similar diagnostic applications have beendeveloped to diagnose problems within the supporting equipment withinthe process plant.

Thus, for example, the AMS™ Suite: Intelligent Device Managerapplication (at least partially disclosed in U.S. Pat. No. 5,960,214entitled “Integrated Communication Network for use in a Field DeviceManagement System”) sold by Emerson Process Management, enablescommunication with and stores data pertaining to field devices toascertain and track the operating state of the field devices. In someinstances, the AMS™ application may be used to communicate with a fielddevice to change parameters within the field device, to cause the fielddevice to run applications on itself such as, for example,self-calibration routines or self-diagnostic routines, to obtaininformation about the status or health of the field device, etc. Thisinformation may include, for example, status information (e.g., whetheran alarm or other similar event has occurred), device configurationinformation (e.g., the manner in which the field device is currently ormay be configured and the type of measuring units used by the fielddevice), device parameters (e.g., the field device range values andother parameters), etc. Of course, this information may be used by amaintenance person to monitor, maintain, and/or diagnose problems withfield devices.

Similarly, many process plants include equipment monitoring anddiagnostic applications such as, for example, the Machinery Health®application provided by CSI Systems, or any other known applicationsused to monitor, diagnose, and optimize the operating state of variousrotating equipment. Maintenance personnel usually use these applicationsto maintain and oversee the performance of rotating equipment in theplant, to determine problems with the rotating equipment, and todetermine when and if the rotating equipment must be repaired orreplaced. Similarly, many process plants include power control anddiagnostic applications such as those provided by, for example, theLiebert and ASCO companies, to control and maintain the power generationand distribution equipment. It is also known to run control optimizationapplications such as, for example, real-time optimizers (RTO+), within aprocess plant to optimize the control activities of the process plant.Such optimization applications typically use complex algorithms and/ormodels of the process plant to predict how inputs may be changed tooptimize operation of the process plant with respect to some desiredoptimization variable such as, for example, profit.

These and other diagnostic and optimization applications are typicallyimplemented on a system-wide basis in one or more of the operator ormaintenance workstations, and may provide preconfigured displays to theoperator or maintenance personnel regarding the operating state of theprocess plant, or the devices and equipment within the process plant.Typical displays include alarming displays that receive alarms generatedby the process controllers or other devices within the process plant,control displays indicating the operating state of the processcontrollers and other devices within the process plant, maintenancedisplays indicating the operating state of the devices within theprocess plant, etc. Likewise, these and other diagnostic applicationsmay enable an operator or a maintenance person to retune a control loopor to reset other control parameters, to run a test on one or more fielddevices to determine the current status of those field devices, tocalibrate field devices or other equipment, or to perform other problemdetection and correction activities on devices and equipment within theprocess plant.

While these various applications and tools are very helpful inidentifying and correcting problems within a process plant, thesediagnostic applications are generally configured to be used only after aproblem has already occurred within a process plant and, therefore,after an abnormal situation already exists within the plant.Unfortunately, an abnormal situation may exist for some time before itis detected, identified and corrected using these tools, resulting inthe suboptimal performance of the process plant for the period of timeduring which the problem is detected, identified and corrected. In manycases, a control operator will first detect that some problem existsbased on alarms, alerts or poor performance of the process plant. Theoperator will then notify the maintenance personnel of the potentialproblem. The maintenance personnel may or may not detect an actualproblem and may need further prompting before actually running tests orother diagnostic applications, or performing other activities needed toidentify the actual problem. Once the problem is identified, themaintenance personnel may need to order parts and schedule a maintenanceprocedure, all of which may result in a significant period of timebetween the occurrence of a problem and the correction of that problem,during which time the process plant runs in an abnormal situationgenerally associated with the sub-optimal operation of the plant.

Additionally, many process plants can experience an abnormal situationwhich results in significant costs or damage within the plant in arelatively short amount of time. For example, some abnormal situationscan cause significant damage to equipment, the loss of raw materials, orsignificant unexpected downtime within the process plant if theseabnormal situations exist for even a short amount of time. Thus, merelydetecting a problem within the plant after the problem has occurred, nomatter how quickly the problem is corrected, may still result insignificant loss or damage within the process plant. As a result, it isdesirable to try to prevent abnormal situations from arising in thefirst place, instead of simply trying to react to and correct problemswithin the process plant after an abnormal situation arises.

One technique that may be used to collect data that enables a user topredict the occurrence of certain abnormal situations within a processplant before these abnormal situations actually arise, with the purposeof taking steps to prevent the predicted abnormal situation before anysignificant loss within the process plant takes place. This procedure isdisclosed in U.S. patent application Ser. No. 09/972,078, entitled “RootCause Diagnostics,” now U.S. Pat. No. 7,085,610, (based in part on U.S.patent application Ser. No. 08/623,569, now U.S. Pat. No. 6,017,143).The entire disclosures of both of these applications are herebyincorporated by reference herein. Generally speaking, this techniqueplaces statistical data collection and processing blocks or statisticalprocessing monitoring (SPM) blocks, in each of a number of devices, suchas field devices, within a process plant. The statistical datacollection and processing blocks collect, for example, process variabledata and determine certain statistical measures associated with thecollected data, such as a mean, a median, a standard deviation, etc.These statistical measures may then be sent to a user and analyzed torecognize patterns suggesting the future occurrence of a known abnormalsituation. Once a particular suspected future abnormal situation isdetected, steps may be taken to correct the underlying problem, therebyavoiding the abnormal situation in the first place.

Other techniques have been developed to monitor and detect problems in aprocess plant. One such technique is referred to as Statistical ProcessControl (SPC). SPC has been used to monitor variables, such as qualityvariables, associated with a process and flag an operator when thequality variable is detected to have moved from its “statistical” norm.With SPC, a small sample of a variable, such as a key quality variable,is used to generate statistical data for the small sample. Thestatistical data for the small sample is then compared to statisticaldata corresponding to a much larger sample of the variable. The variablemay be generated by a laboratory or analyzer, or retrieved from a datahistorian. SPC alarms are generated when the small sample's average orstandard deviation deviates from the large sample's average or standarddeviation, respectively, by some predetermined amount. An intent of SPCis to avoid making process adjustments based on normal statisticalvariation of the small samples. Charts of the average or standarddeviation of the small samples may be displayed to the operator on aconsole separate from a control console.

Another technique analyzes multiple variables and is referred to asmultivariable statistical process control (MSPC). This technique usesalgorithms such as principal component analysis (PCA) and partial leastsquares (PLS) which analyze historical data to create a statisticalmodel of the process. In particular, samples of variables correspondingto normal operation and samples of variables corresponding to abnormaloperation are analyzed to generate a model to determine when an alarmshould be generated. Once the model has been defined, variablescorresponding to a current process may be provided to the model, whichmay generate an alarm if the variables indicate an abnormal operation.

With model-based performance monitoring system techniques, a model isutilized, such as a correlation-based model or a first-principles model,that relates process inputs to process outputs. The model may becalibrated to the actual plant operation by adjusting internal tuningconstants or bias terms. The model can be used to predict when theprocess is moving into an abnormal region and alert the operator to takeaction. An alarm may be generated when there is a significant deviationin actual versus predicted behavior or when there is a big change in acalculated efficiency parameter. Model-based performance monitoringsystems typically cover as small as a single unit operation (e.g. apump, a compressor, a heater, a column, etc.) or a combination ofoperations that make up a process unit (e.g. crude unit, fluid catalyticcracking unit (FCCU), reformer, etc.).

Yet another technique utilizes a model configurable to include multipleregression models corresponding to different operating regions. Thisprocedure is disclosed in U.S. patent application Ser. No. 11/492,467,filed on Jul. 25, 2006 and entitled “Method and System for DetectingAbnormal Operation in a Process Plant.” The entire disclosure of thisapplication is hereby incorporated by reference herein. Generallyspeaking, this technique generates and utilizes the model by determiningif the actual operation deviates from the operation predicted by themodel. If the deviation is significant, this may indicate an abnormaloperation. If the operation moves to a different operating region, a newregression model is developed for that region and the model is updatedto include the new operating region. Further developments of thistechnique have included models for a level regulatory control loop togenerate a prediction of a signal associated with regulatory control ofa level of material in a tank as disclosed in U.S. patent applicationSer. No. 11/492,577, filed on Jul. 25, 2006 and entitled “Method andSystem for Detecting Abnormal Operation of a Level Regulatory ControlLoop,” using a mean signal or other statistical signal generated byprocessing a measured process variable, and analyzing the signal todetermine if it significantly deviates from an expected value asdisclosed in U.S. patent application Ser. No. 11/492,347, now U.S. Pat.No. 7,657,399, filed on Jul. 25, 2006 and entitled “Methods and Systemsfor Detecting Deviation of a Process Variable from Expected Values,” andas disclosed in U.S. patent application Ser. No. 11/492,460, filed onJul. 25, 2006 and entitled “Methods and Systems for Detecting Deviationof a Process Variable from Expected Values.” The entire disclosures ofthese applications are hereby incorporated by reference herein.

While the above techniques may be applied to a variety of processindustries, refining is one industry in which abnormal situationprevention is particularly applicable. More particularly, abnormalsituation prevention is particularly applicable to hydrocrackers as usedin the refining industry. Generally, a hydrocracker unit in a refineryuses hydrogen to “crack” heavier hydrocarbons into lighter hydrocarbons.For example, complex organic molecules (e.g., heavy hydrocarbons) arebroken down into simpler molecules (e.g. light hydrocarbons) by thebreaking of carbon-carbon bonds. The rate of cracking and the endproducts are dependent on the temperature. One particular problemassociated with hydrocrackers is that of temperature runaway, which canoccur in a reactor of the hydrocracker.

SUMMARY OF THE DISCLOSURE

Example methods and systems are disclosed that may facilitate detectingan abnormal operation associated with a hydrocracker. Generallyspeaking, a model to model at least a portion of the hydrocracker may beconfigurable to include multiple regression models corresponding tomultiple different operating regions of the hydrocracker. The model maybe utilized, for example, by determining if the actual operation of thehydrocracker deviates significantly from the operation predicted by themodel. If there is a significant deviation, this may indicate anabnormal operation.

In one embodiment, a method of detecting an abnormal operation of ahydrocracker is disclosed. The method may include collecting first datasets for the hydrocracker while the hydrocracker is in an operatingregion and generating a regression model of the hydrocracker in theoperating region using the first data sets. The first data sets may begenerated from a temperature difference variable between first andsecond cross sections in a reactor of the hydrocracker in the operatingregion. The first data sets may be further generated from a loadvariable of the hydrocracker in the operation region. The regressionmodel may be used to generate a prediction of first data generated fromthe temperature difference variable as a function of second datagenerated from the load variable. Then, the method determines if acorresponding signal generated from the temperature difference variabledeviates from the prediction of the first data generated from thetemperature difference variable to detect an abnormal situation withinthe hydrocracker.

In another embodiment, a method of detecting an abnormal operation of ahydrocracker is disclosed. The method may include collecting a pluralityof first data sets for the hydrocracker while the hydrocracker is in anoperating region and generating a regression model of the hydrocrackerin the operating region for each temperature difference variable usingthe plurality of first data sets. Each of the plurality of first datasets may be generated from a corresponding temperature differencevariable between cross sections in a reactor of the hydrocracker in theoperating region and from a load variable of the hydrocracker in theoperating region. The regression model may be used to generate aprediction of first data generated from each of the temperaturedifference variables as a function of second data generated from theload variable. Then, the method determines if a corresponding signalgenerated from at least one of the temperature difference variablesdeviates from the prediction of the first data generated from thecorresponding temperature difference variable to detect an abnormalsituation within the hydrocracker.

In a further embodiment, a method of facilitating detection of anabnormal operation of a hydrocracker is disclosed. The method mayinclude collecting first data sets while the hydrocracker is in a firstoperating region, and collecting second data sets while the hydrocrackeris in a second operating region. The first and second data sets may begenerated from a temperature difference variable between first andsecond cross sections in a reactor of the hydrocracker and furthergenerated from a load variable of the hydrocracker. A first regressionmodel of the process in the first operating region may be generatedusing the first data sets, and a second regression model of thehydrocracker may be generated in the second operating region using thesecond data sets. The method may determine a first range in which thefirst regression model is valid and a second range in which the secondregression model is valid. A model of the hydrocracker may be generatedto include the first regression model and revised to include the secondregression model for the second range along with the first regressionmodel in the first range.

In yet another embodiment, a system for detecting an abnormal operationof a hydrocracker in a process plant is disclosed. The system mayinclude a configurable model of the hydrocracker in the process plant.The configurable model may include a first regression model in a firstrange corresponding to a first operating region of the hydrocracker. Theconfigurable model may be subsequently configured to include a secondregression model in a second range corresponding to a second operatingregion different than the first operating region. The configurable modelmay generate a prediction of a temperature difference variable value asa function of a load variable value. The temperature difference variablevalue may be generated from a temperature difference between first andsecond cross sections in a reactor of the hydrocracker. The system mayfurther include a deviation detector coupled to the configurable model.The deviation detector may determine if the temperature differencevariable value differs from the predicted temperature differencevariable value by comparing a difference between the temperaturedifference variable value and predicted temperature difference variablevalue to a threshold.

In a yet further embodiment, a system for detecting an abnormaloperation in a hydrocracker in a process plant is disclosed. The systemmay include a configurable model of the hydrocracker in the processplant. The configurable model may include a regression model in a rangecorresponding to an operating region of the hydrocracker. Theconfigurable model may generate a prediction of a temperature differencevariable value as a function of a load variable value. The temperaturedifference variable value may be generated from a temperature differencebetween first and second cross sections in a reactor of thehydrocracker. The system may further include a deviation detectorcoupled to the configurable model. The deviation detector may determineif the temperature difference variable value differs from the predictedtemperature difference variable value by comparing a difference betweenthe temperature difference variable value and predicted temperaturedifference variable value to a threshold. The system may also include anintegration application that creates a representation of thehydrocracker for use in viewing the temperature difference variablevalue and for use in viewing an indication of an abnormal operation ofthe hydrocracker in response to comparing the difference between thetemperature difference variable value and the predicted temperaturedifference variable value to a threshold.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an exemplary block diagram of a process plant having adistributed control and maintenance network including one or moreoperator and maintenance workstations, controllers, field devices andsupporting equipment;

FIG. 2A is an exemplary block diagram of a portion of the process plantof FIG. 1, illustrating communication interconnections between variouscomponents of an abnormal situation prevention system located within ahydrocracker of the process plant;

FIG. 2B is an exemplary block diagram of the hydrocracker of FIG. 2A;

FIG. 3A is an example abnormal operation detection (AOD) system thatutilizes one or more regression models;

FIG. 3B is an example regression block of the AOD system of FIG. 3A;

FIG. 4 is flow diagram of an example method that may be implementedusing the example AOD system of FIGS. 3A and 3B;

FIG. 5 is a flow diagram of an example method for initially training themodel of FIGS. 3A and 3B;

FIG. 6A is a graph showing a plurality of data sets that may becollected during a LEARNING state an AOD system and used by the model ofFIG. 3B to develop a regression model;

FIG. 6B is a graph showing an initial regression model developed usingthe plurality of data sets of FIG. 6A;

FIG. 7 is a flow diagram of an example method that may be implementedusing the example abnormal operation detection system of FIGS. 3A and3B;

FIG. 8A is a graph showing a received data set and a correspondingpredicted value generated during a MONITORING state of an AOD system bythe model of FIG. 3B;

FIG. 8B is a graph showing another received data set and anothercorresponding predicted value generated by the model of FIG. 3B;

FIG. 8C is a graph showing a received data set that is out of a validityrange of the model of FIG. 3B;

FIG. 9A is a graph showing a plurality of data sets in differentoperating region collected during a LEARNING state of an AOD system andthat may be used by the model of FIG. 3 to develop a second regressionmodel in a different operating region;

FIG. 9B is a graph showing a second regression model developed using theplurality of data sets of FIG. 9A;

FIG. 9C is a graph showing an updated model and its range of validity,and also showing a received data set and a corresponding predicted valuegenerated during a MONITORING state of an AOD system;

FIG. 10 is a flow diagram of an example method for updating the model ofFIG. 3B;

FIG. 11 is an example state transition diagram corresponding to analternative operation of an AOD system such as the AOD systems of FIGS.3A and 3B;

FIG. 12 is a flow diagram of an example method of operation in aLEARNING state of an AOD system;

FIG. 13 is a flow diagram of an example method for updating a model ofan AOD system;

FIG. 14 is a flow diagram of an example method of operation in aMONITORING state of an AOD system;

FIG. 15 is an exemplary depiction of a display that may be provided by agraphical user interface to enable a user to view temperature differencevariables for a hydrocracker and monitor the occurrence of abnormalconditions;

FIG. 16 is an exemplary depiction of a display that may be provided by agraphical user interface to enable a user to view an alert generated inresponse to detection of an abnormal condition;

FIG. 17 is an exemplary depiction of a display that may be provided by agraphical user interface to enable a user to view a message associatedwith the abnormal condition;

FIG. 18 is an exemplary depiction of a display that may be provided by agraphical user interface to enable a user to view further informationassociated with the abnormal condition; and

FIG. 19 is an exemplary depiction of a display that may be provided by agraphical user interface to enable a user to view and configure the AODsystem.

DETAILED DESCRIPTION

Referring now to FIG. 1, an example process plant 10 in which anabnormal situation prevention system may be implemented includes anumber of control and maintenance systems interconnected together withsupporting equipment via one or more communication networks. Inparticular, the process plant 10 of FIG. 1 includes one or more processcontrol systems 12 and 14. The process control system 12 may be atraditional process control system such as a PROVOX or RS3 system or anyother control system which includes an operator interface 12A coupled toa controller 12B and to input/output (I/O) cards 12C which, in turn, arecoupled to various field devices such as analog and Highway AddressableRemote Transmitter (HART) field devices 15. The process control system14, which may be a distributed process control system, includes one ormore operator interfaces 14A coupled to one or more distributedcontrollers 14B via a bus, such as an Ethernet bus. The controllers 14Bmay be, for example, DeltaV™ controllers sold by Emerson ProcessManagement of Austin, Tex. or any other desired type of controllers. Thecontrollers 14B are connected via I/O devices to one or more fielddevices 16, such as for example, HART or Fieldbus field devices or anyother smart or non-smart field devices including, for example, thosethat use any of the PROFIBUS®, WORLDFIP®, Device-Net®, AS-Interface andCAN protocols. As is known, the field devices 16 may provide analog ordigital information to the controllers 14B related to process variablesas well as to other device information. The operator interfaces 14A maystore and execute tools 17, 19 available to the process control operatorfor controlling the operation of the process including, for example,control optimizers, diagnostic experts, neural networks, tuners, etc.

Still further, maintenance systems, such as computers executing the AMS™Suite: Intelligent Device Manager application or any other devicemonitoring and communication applications may be connected to theprocess control systems 12 and 14 or to the individual devices thereinto perform maintenance and monitoring activities. For example, amaintenance computer 18 may be connected to the controller 12B and/or tothe devices 15 via any desired communication lines or networks(including wireless or handheld device networks) to communicate withand, in some instances, reconfigure or perform other maintenanceactivities on the devices 15. Similarly, maintenance applications suchas the AMS™ application may be installed in and executed by one or moreof the user interfaces 14A associated with the distributed processcontrol system 14 to perform maintenance and monitoring functions,including data collection related to the operating status of the devices16.

The process plant 10 also includes various rotating equipment 20, suchas turbines, motors, etc. which are connected to a maintenance computer22 via some permanent or temporary communication link (such as a bus, awireless communication system or hand held devices which are connectedto the equipment 20 to take readings and are then removed). Themaintenance computer 22 may store and execute known monitoring anddiagnostic applications 23 provided by, for example, CSI (an EmersonProcess Management Company) or any other known applications used todiagnose, monitor and optimize the operating state of the rotatingequipment 20. Maintenance personnel usually use the applications 23 tomaintain and oversee the performance of rotating equipment 20 in theplant 10, to determine problems with the rotating equipment 20 and todetermine when and if the rotating equipment 20 must be repaired orreplaced. In some cases, outside consultants or service organizationsmay temporarily acquire or measure data pertaining to the equipment 20and use this data to perform analyses for the equipment 20 to detectproblems, poor performance or other issues effecting the equipment 20.In these cases, the computers running the analyses may not be connectedto the rest of the system 10 via any communication line or may beconnected only temporarily.

Similarly, a power generation and distribution system 24 having powergenerating and distribution equipment 25 associated with the plant 10 isconnected via, for example, a bus, to another computer 26 which runs andoversees the operation of the power generating and distributionequipment 25 within the plant 10. The computer 26 may execute knownpower control and diagnostics applications 27 such a as those providedby, for example, Liebert and ASCO or other companies to control andmaintain the power generation and distribution equipment 25. Again, inmany cases, outside consultants or service organizations may use serviceapplications that temporarily acquire or measure data pertaining to theequipment 25 and use this data to perform analyses for the equipment 25to detect problems, poor performance or other issues effecting theequipment 25. In these cases, the computers (such as the computer 26)running the analyses may not be connected to the rest of the system 10via any communication line or may be connected only temporarily.

As illustrated in FIG. 1, a computer system 30 implements at least aportion of an abnormal situation prevention system 35, and inparticular, the computer system 30 stores and implements a configurationapplication 38 and, optionally, an abnormal operation detection system42, which will be described in more detail below. Additionally, thecomputer system 30 may implement an alert/alarm application 43.

Generally speaking, the abnormal situation prevention system 35 maycommunicate with abnormal operation detection systems (not shown inFIG. 1) optionally located in the field devices 15, 16, the controllers12B, 14B, the rotating equipment 20 or its supporting computer 22, thepower generation equipment 25 or its supporting computer 26, and anyother desired devices and equipment within the process plant 10, and/orthe abnormal operation detection system 42 in the computer system 30, toconfigure each of these abnormal operation detection systems and toreceive information regarding the operation of the devices or subsystemsthat they are monitoring. The abnormal situation prevention system 35may be communicatively connected via a hardwired bus 45 to each of atleast some of the computers or devices within the plant 10 or,alternatively, may be connected via any other desired communicationconnection including, for example, wireless connections, dedicatedconnections which use OPC, intermittent connections, such as ones whichrely on handheld devices to collect data, etc. Likewise, the abnormalsituation prevention system 35 may obtain data pertaining to the fielddevices and equipment within the process plant 10 via a LAN or a publicconnection, such as the Internet, a telephone connection, etc.(illustrated in FIG. 1 as an Internet connection 46) with such databeing collected by, for example, a third party service provider.Further, the abnormal situation prevention system 35 may becommunicatively coupled to computers/devices in the plant 10 via avariety of techniques and/or protocols including, for example, Ethernet,Modbus, HTML, XML, proprietary techniques/protocols, etc. Thus, althoughparticular examples using OPC to communicatively couple the abnormalsituation prevention system 35 to computers/devices in the plant 10 aredescribed herein, one of ordinary skill in the art will recognize that avariety of other methods of coupling the abnormal situation preventionsystem 35 to computers/devices in the plant 10 can be used as well.

FIG. 2A illustrates a portion 50 of the example process plant 10 of FIG.1 for the purpose of describing one manner in which the abnormalsituation prevention system 35 and/or the alert/alarm application 43 maycommunicate with a hydrocracker in the portion 50 of the example processplant 10. In one example, the process plant 10 or portion 50 of theprocess plant may be a refinery plant for breaking complex moleculesdown into simpler molecules (e.g., “cracking” heavier hydrocarbons intolighter hydrocarbons). While FIG. 2A illustrates communications betweenthe abnormal situation prevention system 35 and one or more abnormaloperation detection systems within the hydrocracker, it will beunderstood that similar communications can occur between the abnormalsituation prevention system 35 and other devices and equipment withinthe process plant 10, including any of the devices and equipmentillustrated in FIG. 1.

The portion 50 of the process plant 10 illustrated in FIG. 2A includes adistributed process control system 54 having one or more processcontrollers 60 connected to one or more reactors 64 and 66 of ahydrocracker 62 via input/output (I/O) cards or devices 68 and 70, whichmay be any desired types of I/O devices conforming to any desiredcommunication or controller protocol. Additionally, the hydrocracker 62and/or the reactors 64, 66 of the hydrocracker 62 may conform to anydesired open, proprietary or other communication or programmingprotocol, it being understood that the I/O devices 68 and 70 must becompatible with the desired protocol used by the hydrocracker 62 andreactors 64, 66.

Although not shown in detail, the hydrocracker 62 and reactors 64, 66may include any number of additional devices, including, but not limitedto, field devices, HART devices, sensors, valves, transmitters,positioners, etc. any or all of which may be used to measure and/orcollect data, such as process variable data related to the hydrocracker62, and the reactors 64, 66, and the operation thereof. For example, asdiscussed herein, a temperature difference variable is monitored withrespect to a reactor. The temperature difference variable may be derivedfrom temperature sensors, transmitters or other devices that measure thetemperature at one or more locations of various cross-sections or “beds”in the reactor. These devices, or additional devices, may be used tocalculate the weighted average bed temperature (WABT) at eachcross-section and determine the temperature difference variable ΔTbetween the various cross-sections. As such, although the followinggenerally described the temperature difference variable as beingprovided from the hydrocracker 62 and its reactors 64, 66, it should beunderstood that the temperature difference variable and/or thetemperature measurements used to derived the temperature differencevariable, may, more particularly, be provided from the devices that arepart of the hydrocracker 62 and reactors 64, 66, such as temperaturesensors, transmitters, etc.

In any event, one or more user interfaces or computers 72 and 74 (whichmay be any types of personal computers, workstations, etc.) accessibleby plant personnel such as configuration engineers, process controloperators, maintenance personnel, plant managers, supervisors, etc. arecoupled to the process controllers 60 via a communication line or bus 76which may be implemented using any desired hardwired or wirelesscommunication structure, and using any desired or suitable communicationprotocol such as, for example, an Ethernet protocol. In addition, adatabase 78 may be connected to the communication bus 76 to operate as adata historian that collects and stores configuration information aswell as on-line process variable data, parameter data, status data, andother data associated with the process controllers 60 and thehydrocracker 62, including the reactors 64, 66, within the process plant10. Thus, the database 78 may operate as a configuration database tostore the current configuration, including process configurationmodules, as well as control configuration information for the processcontrol system 54 as downloaded to and stored within the processcontrollers 60 and devices of the hydrocracker 62, including thereactors 64, 66. Likewise, the database 78 may store historical abnormalsituation prevention data, including statistical data collected by thehydrocracker 62, including the reactors 64, 66 (or more particularlydevices of the hydrocracker 62 and/or reactors 64, 66), statistical datadetermined from process variables collected by the hydrocracker 62,including the reactors 64, 66 (or more particularly devices of thehydrocracker 62 and/or reactors 64, 66), and other types of data thatwill be described below.

While the process controllers 60, I/O devices 68 and 70, hydrocracker62, the reactors 64, 66 and devices of the hydrocracker 62 and reactors64, 66 are typically located down within and distributed throughout thesometimes harsh plant environment, the workstations 72 and 74, and thedatabase 78 are usually located in control rooms, maintenance rooms orother less harsh environments easily accessible by operators,maintenance personnel, etc. Although only one hydrocracker 62 is shownwith only two reactors 64, 66, it should be understood that a processplant 10 may have multiple hydrocrackers along with various other typesof equipment such as that shown in FIG. 1. It should be furtherunderstood that a hydrocracker may include any number of reactors. Theabnormal situation prevision techniques described herein may be equallyapplied to any of a number of reactors or hydrocrackers.

Generally speaking, the process controllers 60 store and execute one ormore controller applications that implement control strategies using anumber of different, independently executed, control modules or blocks.The control modules may each be made up of what are commonly referred toas function blocks, wherein each function block is a part or asubroutine of an overall control routine and operates in conjunctionwith other function blocks (via communications called links) toimplement process control loops within the process plant 10. As is wellknown, function blocks, which may be objects in an object-orientedprogramming protocol, typically perform one of an input function, suchas that associated with a transmitter, a sensor or other processparameter measurement device, a control function, such as thatassociated with a control routine that performs PID, fuzzy logic, etc.control, or an output function, which controls the operation of somedevice, such as a valve, to perform some physical function within theprocess plant 10. Of course, hybrid and other types of complex functionblocks exist, such as model predictive controllers (MPCs), optimizers,etc. It is to be understood that while the Fieldbus protocol and theDeltaV™ system protocol use control modules and function blocks designedand implemented in an object-oriented programming protocol, the controlmodules may be designed using any desired control programming schemeincluding, for example, sequential function blocks, ladder logic, etc.,and are not limited to being designed using function blocks or any otherparticular programming technique.

As illustrated in FIG. 2A, the maintenance workstation 74 includes aprocessor 74A, a memory 74B and a display device 74C. The memory 74Bstores the abnormal situation prevention application 35 and thealert/alarm application 43 discussed with respect to FIG. 1 in a mannerthat these applications can be implemented on the processor 74A toprovide information to a user via the display 74C (or any other displaydevice, such as a printer).

Each of one or more of the reactors 64, 66 and the hydrocracker 62,and/or the devices of the reactors 64, 66 and hydrocracker 62 inparticular, may include a memory (not shown) for storing routines suchas routines for implementing statistical data collection pertaining toone or more process variables sensed by sensing device and/or routinesfor abnormal operation detection, which will be described below. Each ofone or more of the reactors 64, 66 and the hydrocracker 62, and/or someor all of the devices thereof in particular, may also include aprocessor (not shown) that executes routines such as routines forimplementing statistical data collection and/or routines for abnormaloperation detection. Statistical data collection and/or abnormaloperation detection need not be implemented by software. Rather, one ofordinary skill in the art will recognize that such systems may beimplemented by any combination of software, firmware, and/or hardwarewithin one or more field devices and/or other devices.

As shown in FIG. 2A, the reactors 64, 66 (and potentially some or allreactors in a hydrocracker 62) include abnormal operation detectionblocks 80 and 82, which will be described in more detail below. Whilethe blocks 80 and 82 of FIG. 2 are illustrated as being located in oneof the reactors 64, 66, these or similar blocks could be located in anynumber of reactors or within various other equipment and devices in thehydrocracker 62 or the reactors 64, 66, could be located in otherdevices, such as the controller 60, the I/O devices 68, 70 or any of thedevices illustrated in FIG. 1. Additionally, the blocks 80 and 82 couldbe in any subset of the reactors 64, 66, such as in one or more devicesof the reactors 64, 66, for example (e.g., temperature sensor,temperature transmitter, etc.).

Generally speaking, the blocks 80 and 82 or sub-elements of theseblocks, collect data, such a process variable data, from the device inwhich they are located and/or from other devices. For example, theblocks 80, 82 may collect the temperature difference variable fromdevices within the hydrocracker 62 or the reactors 64, 66, such as atemperature sensor, a temperature transmitter, or other devices, or maydetermine the temperature difference variable from temperaturemeasurements from the devices. Additionally, the blocks 80 and 82 orsub-elements of these blocks may process the variable data and performan analysis on the data for any number of reasons. For example, theblock 80, which is illustrated as being associated with a reactor 64(Reactor_1), may have a runaway temperature detection routine whichanalyzes temperature difference process variable data to determine if atemperature difference between two cross-sections in the reactor 64 isincreasing, which may be indicative of a runaway temperature.

FIG. 2B is a more detailed example of the hydrocracker 62 shown in FIG.2A. As seen in FIG. 2B, each reactor 64, 66 includes multiplecross-sections or “beds” across the reactor at which a cross-sectionaltemperature is measured. In one example, the temperature of eachcross-section is a Weighted Average Bed Temperature (WABT). The WABT foreach cross-section may be provided as a weighted average of multipletemperature measurements T_(i) that are measured at the cross-section.Each temperature has an associated weight (w_(i)), which may be providedas a user input. The weighted average bed temperature at a givencross-section may be calculated by the following formula:

${WABT} = \frac{\sum\limits_{i}{w_{i}T_{i}}}{\sum\limits_{i}w_{i}}$

A temperature difference variable (ΔT) between each pair of WABT's iscalculated. Generally, there are n WABT's, and n−1 ΔT's. An increase inany of the ΔT's could indicate a runaway temperature condition. In oneexample, the abnormal situation prevention technique for a hydrocrackerlearns baseline values for each of the ΔT's, and during monitoring, ifthe new value for any of the ΔT's significantly deviates from thebaseline value, for example, by more than a certain threshold, atemperature runaway indicator, such as an alert/alarm, is generated. Thealert may specify in which of the of the ΔT's (ΔT₁, ΔT₂, . . . ) theabnormal condition has occurred. However, the values of the ΔT's mayalso change as a function of some load variable during normal operatingconditions. As described further below, a regression algorithm may beused to model the value of each ΔT as a function of that load variable.While monitoring the reactors during operation, the runaway temperaturecondition may be detected if the actual and predicted values for ΔTdiffer substantially, for example by a threshold. The general operationof hydrocrackers is generally understood by those of ordinary skill inthe art and need not be further described.

Referring again to FIG. 2A, the block 80 may include a set of one ormore statistical process monitoring (SPM) blocks or units such as blocksSPM1-SPM4 which may collect process variable or other data within thereactor and perform one or more statistical calculations on thecollected data to determine, for example, a mean, a median, a standarddeviation, a root-mean-square (RMS), a rate of change, a range, aminimum, a maximum, etc. of the collected data and/or to detect eventssuch as drift, bias, noise, spikes, etc., in the collected data. Thespecific statistical data generated, and the method in which it isgenerated is not critical. Thus, different types of statistical data canbe generated in addition to, or instead of, the specific types describedabove. Additionally, a variety of techniques, including knowntechniques, can be used to generate such data. The term statisticalprocess monitoring (SPM) block is used herein to describe functionalitythat performs statistical process monitoring on at least one processvariable or other process parameter, such as the temperature differencevariable, and may be performed by any desired software, firmware orhardware within the device or even outside of a device for which data iscollected. It will be understood that, because the SPMs are generallylocated in the devices where the device data is collected, the SPMs canacquire quantitatively more and qualitatively more accurate processvariable data. As a result, the SPM blocks are generally capable ofdetermining better statistical calculations with respect to thecollected process variable data than a block located outside of thedevice in which the process variable data is collected.

It is to be understood that although the blocks 80 and 82 are shown toinclude SPM blocks in FIG. 2A, the SPM blocks may instead be stand-aloneblocks separate from the blocks 80 and 82, and may be located in thesame reactor as the corresponding block 80 or 82 or may be in adifferent device. The SPM blocks discussed herein may comprise knownFOUNDATION™ Fieldbus SPM blocks, or SPM blocks that have different oradditional capabilities as compared with known FOUNDATION™ Fieldbus SPMblocks. The term statistical process monitoring (SPM) block is usedherein to refer to any type of block or element that collects data, suchas process variable data, and performs some statistical processing onthis data to determine a statistical measure, such as a mean, a standarddeviation, etc. As a result, this term is intended to cover software,firmware, hardware and/or other elements that perform this function,whether these elements are in the form of function blocks, or othertypes of blocks, programs, routines or elements and whether or not theseelements conform to the FOUNDATION™ Fieldbus protocol, or some otherprotocol, such as Profibus, HART, CAN, etc. protocol. If desired, theunderlying operation of blocks 80, 82 may be performed or implemented atleast partially as described in U.S. Pat. No. 6,017,143, which is herebyincorporated by reference herein.

It is to be further understood that although the blocks 80 and 82 areshown to include SPM blocks in FIG. 2A, SPM blocks are not required ofthe blocks 80 and 82. For example, abnormal operation detection routinesof the blocks 80 and 82 could operate using process variable data notprocessed by an SPM block. As another example, the blocks 80 and 82could each receive and operate on data provided by one or more SPM blocklocated in other devices. As yet another example, the process variabledata could be processed in a manner that is not provided by many typicalSPM blocks. As just one example, the process variable data could befiltered by a finite impulse response (FIR) or infinite impulse response(IIR) filter such as a bandpass filter or some other type of filter. Asanother example, the process variable data could be trimmed so that itremained in a particular range. Of course, known SPM blocks could bemodified to provide such different or additional processingcapabilities.

The blocks 80, 82 of FIG. 2A, which are illustrated as being associatedwith a reactor 64, 66, and more particularly with a temperature sensoror transmitter thereof, for example, may each have a runaway temperaturedetection unit that analyzes the process variable data collected by thetemperature sensor or transmitter to determine if a temperaturedifference variable associated with the reactor significantly deviatesfrom the expected temperature difference variable. In addition, theblock 82 may include one or more SPM blocks or units such as blocksSPM1-SPM4 which may collect process variable or other data within thetransmitter and perform one or more statistical calculations on thecollected data to determine, for example, a mean, a median, a standarddeviation, etc. of the collected data. While the blocks 80 and 82 areillustrated as including four SPM blocks each, the blocks 80 and 82could have any other number of SPM blocks therein for collecting anddetermining statistical data.

Overview of an Abnormal Operation Detection (AOD) System in aHydrocracker

FIG. 3A is a block diagram of an example abnormal operation detection(AOD) system 100 that could be utilized in the abnormal operationdetection blocks 80, 82 or as the abnormal operation detection system 42of FIG. 2 for a hydrocracker reactor abnormal situation preventionmodule. The AOD system 100 may be used to detect abnormal operations,also referred to as abnormal situations, that have occurred or areoccurring in the hydrocracker 62 or hydrocracker reactor 64, 66, such asrunaway temperatures. In addition, the AOD system 100 may be used topredict the occurrence of abnormal operations within the hydrocracker 62or reactors 64, 66 before these abnormal operations actually arise, withthe purpose of taking steps to prevent the predicted abnormal operationbefore any significant loss within the reactors 64, 66, the hydrocracker66 or the process plant 10 takes place, for example, by operating inconjunction with the abnormal situation prevention system 35.

In one example, each reactor may have a corresponding AOD system 100,though it should be understood that a common AOD system may be used formultiple reactors or for the hydrocracker as a whole. As noted above,there are generally n WABT's 102, and n−1 ΔT's 104, where an increase inany ΔT's 104 could indicate a runaway temperature condition. However,because it is also possible that the ΔT's 104 could change during normaloperating conditions as a function of some load variable 106, the AODsystem 100 learns the normal or baseline ΔT values 104 for a range ofvalues for the load variable 106.

As shown in FIG. 3A, the load variable 106 and each ΔT variable 104 arefed into respective regression blocks 108. The AOD system 100 includesregression blocks for each temperature difference variable ΔT_(N-1).During the learning phase, which is described in more detail below, eachregression block 108 creates a regression model to predict datagenerated from the corresponding ΔT as a function of data generated fromthe load variable. The data generated from ΔT and data generated fromthe load variable may include ΔT and load variable data, ΔT and loadvariable data that has been filtered or otherwise processed, statisticaldata generated from ΔT and load variable data, etc. During themonitoring phase, which is also described in more detail below, theregression model predicts a value for its data generated from ΔT given avalue of data generated from the load variable during operation of thereactor. Each regression block outputs a status based upon a deviation,if any, between the predicted value of data generated from ΔT and amonitored value of data generated from ΔT for a given value of datagenerated from the load variable. For example, if the monitored value ofΔT significantly deviates from the predicted value of ΔT, the regressionblock 108 may output a status of “Up”, which is an indication that arunaway temperature is occurring for the corresponding temperaturedifference variable ΔT. Otherwise, the regression block 108 may outputthe status as “Normal”. In another example, if the monitored mean valueof ΔT significantly deviates from a predicted mean value of ΔT, theregression block 108 may output the status of “Up”, or otherwise outputthe status as “Normal”. A status decision block 110 receives the statusfrom each regression block 108 and determines the status of the reactor.If any of the regression blocks 108 have a status of “Up”, the status ofthe reactor is “runaway temperature” for the corresponding temperaturedifference value ΔT. However, it should also be understood that thestatus decision block 110 may receive the status from other regressionblocks 108, such as regression blocks 108 for other reactors, anddetermine the status of the hydrocracker 62. The monitored value of datagenerated from ΔT may be derived by a variety of methods, includingsensor measurements, modeled measurements based on other monitoredprocess measurements, statistical measurements, analysis results, etc.As discussed further below, the monitored temperature differencevariable may be the raw monitored values of the temperature differencevariable, an output of an SPM block or other values generated from thetemperature difference variable.

FIG. 3B is a block diagram of an example of a regression block 108 shownin FIG. 3A. As shown in FIG. 3B, the regression block 108 includes afirst SPM block 112 and a second SPM block 114 each coupled to a model116. The first SPM block 112 receives the load variable and generatesfirst statistical data from the load variable. The first statisticaldata could be any of various kinds of statistical data such as meandata, median data, standard deviation data, rate of change data, rangedata, etc., calculated from the load variable. Such data could becalculated based on a sliding window of the load variable data or basedon non-overlapping windows of the load variable data. As one example,the first SPM block 112 may generate mean and standard deviation dataover a user-specified sample window size, such as a most recent loadvariable sample and 49 previous samples of the load variable. In thisexample, a mean load variable value and a standard deviation loadvariable value may be generated for each new load variable samplereceived by the first SPM block 112. As another example, the first SPMblock 112 may generate mean and standard deviation data usingnon-overlapping time periods. In this example, a window of five minutes(or some other suitable time period) could be used, and a mean and/orstandard deviation load variable value would thus be generated everyfive minutes. In a similar manner, the second SPM block 114 receives thetemperature difference ΔT between cross sections of the reactor (e.g.,between WABT₁ and WABT₂) as a variable and generates second statisticaldata from the temperature difference variable in a manner similar to theSPM block 112, such as mean and standard deviation data over a specifiedsample window.

The model 116 includes a load variable input, which is an independentvariable input (x), from the SPM 112 and a temperature differencevariable input, which is a dependent variable input (y), from the SPM114. As will be described in more detail below, the model 116 may betrained using a plurality of data sets (x, y), to model the temperaturedifference variable as a function of the load variable. For purposes ofexplaining the operation of the AOD system, the temperature differencevariable ΔT is now described as the temperature difference variable Y,and references to both ΔT and Y may be used interchangeably to refer toany of the temperature difference variables ΔT₁-ΔT_(N-1). The model 116may use the mean, standard deviation or other statistical measure of theload variable (X) and the temperature difference variable (Y) from theSPM's 112, 114 as the independent and dependent variable inputs (x, y)for regression modeling. For example, the means of the load variable andthe temperature difference variable may be used as the (x, y) point inthe regression modeling, and the standard deviation may be modeled as afunction of the load variable and used to determine the threshold atwhich an abnormal situation is detected during the monitoring phase. Assuch, it should be understood that while the AOD system 100 is describedas modeling the temperature difference variable as a function of theload variable, the AOD system 100 may model various data generated fromthe temperature difference variable as a function of various datagenerated from the load variable based on the independent and dependentinputs provided to the regression model, including, but not limited to,temperature difference and load variable data, statistical datagenerated from the temperature difference and load variable data, andtemperature difference and load variable data that has been filtered orotherwise processed. Further, while the AOD system 100 is described aspredicting values of the temperature difference variable and comparingthe predicted values to monitored values of the temperature differencevariable, the predicted and monitored values may include variouspredicted and monitored values generated from the temperature differencevariable, such as predicted and monitored temperature difference data,predicted and monitored statistical data generated from the temperaturedifference data, and predicted and monitored temperature difference datathat has been filtered or otherwise processed.

As will also be described in more detail below, the model 116 mayinclude one or more regression models, with each regression modelprovided for a different operating region. As such, multiple regressionmodels may be provided for each temperature difference variable (ΔT₁,ΔT₂, . . . , ΔT_(N-1)), with the regression models for each temperaturedifference variable corresponding to different operating regionsassociated with the hydrocracker in general and/or with a reactor inparticular. Each regression model may utilize a function to model thedependent temperature difference variable as a function of theindependent load variable over some range of the load variable. Theregression model may comprise a linear regression model, for example, orsome other regression model. Generally, a linear regression modelcomprises some linear combination of functions f(X), g(X), h(X), . . . .For modeling an industrial process, a typically adequate linearregression model may comprise a first order function of X (e.g.,Y=m*X+b) or a second order function of X (e.g., Y=a*X²+b*X+c).

In the example shown in FIG. 3B, the (x, y) points are stored during thelearning phase. At the end of the learning phase, the regressioncoefficients are calculated to develop a regression model to predict thetemperature difference variable as a function of the load variable. Themaximum and minimum values of the load variable used to develop theregression model are also stored. The model 116 may be calculated as afunction of observed load variable values (x) and corresponding observedtemperature difference variable values (y) (e.g., A=(X^(T)X)⁻¹X^(T)Y).In one example, the regression fits a polynomial of order p, such thatpredicted values (y_(P)) for the temperature difference variable Y maybe calculated based on the load variable values (x) (e.g., y_(P)=a₀+a₁+. . . +a_(p)x^(p)). Generally, the order of the polynomial p would be auser input, though other algorithms may be provided that automate thedetermination of the order of the polynomial. Of course, other types offunctions may be utilized as well such as higher order polynomials,sinusoidal functions, logarithmic functions, exponential functions,power functions, etc.

After it has been trained, the model 116 may be utilized by thedeviation detector 118 to generate a predicted value (y_(P)) of thedependent temperature difference variable Y based on a given independentload variable input (x) during a monitoring phase. The deviationdetector 118 further utilizes a monitored temperature differencevariable input (y) and the independent load variable input (x) to themodel 116. Generally speaking, the deviation detector 118 calculates thepredicted value (y_(P)) for a particular load variable value and usesthe predicted value as the “normal” or “baseline” temperaturedifference. The deviation detector 118 compares the monitoredtemperature difference variable value (y) to the predicted temperaturedifference value (y_(P)) to determine if the monitored temperaturedifference variable value (y) is significantly deviating from thepredicted temperature difference value (y_(P)) (e.g., Δy=y−y_(P)). Ifthe monitored temperature difference variable value (y) is significantlydeviating from the predicted value (y_(P)), this may indicate that anabnormal situation has occurred, is occurring, or may occur in the nearfuture, and thus the deviation detector 118 may generate an indicator ofthe deviation. For example, if the monitored ΔT value (y) is higher thanthe predicted ΔT value (y_(P)) and the difference exceeds a threshold,an indication of an abnormal situation (e.g., “Up”) may be generated. Ifnot, the status is “normal”. In some implementations, the indicator ofan abnormal situation may comprise an alert or alarm.

In addition to monitoring the hydrocracker for abnormal situations, thedeviation detector 118 may also check to see if the load variable iswithin the limits seen during the development and training of the model.For example, during the monitoring phase the deviation detector 118monitors whether a given value for the load variable is within theoperating range of the regression model as determined by the minimum andmaximum values of the load variable used during the learning phase ofthe model. If the load variable value is outside of the limits, thedeviation detector 118 may output a status of “Out of Range” or otherindication that the load variable is outside of the operating region forthe regression model. The regression block 108 may either await an inputfrom a user to develop and train a new regression model for the newoperating region or automatically develop and train a new regressionmodel for the new operating region, examples of which are providedfurther below.

One of ordinary skill in the art will recognize that the AOD system 100and the regression block 108 can be modified in various ways. Forexample, the SPM blocks 112 and 114 could be omitted, and the raw valuesof the load variable and temperature difference value are provideddirectly to the model 116 as the (x, y) points used for regressionmodeling and provided directly to the deviation detector 118 formonitoring. As another example, other types of processing in addition toor instead of the SPM blocks 112 and 114 could be utilized. For example,the process variable data could be filtered, trimmed, etc., prior to theSPM blocks 112 and 114, or rather than utilizing the SPM blocks 112 and114.

Additionally, although the model 116 is illustrated as having a singleindependent load variable input (x), a single dependent temperaturedifference variable input (y), and a single predicted value (y_(P)), themodel 116 could include a regression model that models multipletemperature difference variables as a function of multiple loadvariables. For example, the model 116 could comprise a multiple linearregression (MLR) model, a principal component regression (PCR) model, apartial least squares (PLS) model, a ridge regression (RR) model, avariable subset selection (VSS) model, a support vector machine (SVM)model, etc.

The AOD system 100 could be implemented wholly or partially in ahydrocracker reactor 64, 66 or a device of the reactor 64, 66 orhydrocracker. As just one example, the SPM blocks 112 and 114 could beimplemented in a temperature sensor or temperature transmitter of thereactor 64 and the model 116 and/or the deviation detector 118 could beimplemented in the controller 60 or some other device. In one particularimplementation, the AOD system 100 could be implemented as a functionblock, such as a function block to be used in system that implements aFieldbus protocol. Such a function block may or may not include the SPMblocks 112 and 114. In another implementation, each of at least some ofthe blocks 108, 110, 112, 114, 116 and 118 may be implemented as afunction block. For example, the blocks 112, 114, 116, 118 may beimplemented as function blocks of a regression function block 108.However, the functions of each blocks may be distributed in a variety ofmanners. For example, the regression model 116 may provide the output(y_(P)) to the deviation detector 118, rather than the deviationdetector 118 executing the regression model 116 to provide theprediction of the temperature difference variable (y_(P)). In thisimplementation, after it has been trained, the model 116 may be used togenerate a predicted value (y_(P)) of the monitored temperaturedifference variable value (y) based on a given independent load variableinput (x). The output (y_(P)) of the model 116 is provided to thedeviation detector 118. The deviation detector 118 receives the output(y_(P)) of the regression model 116 as well as the dependent variableinput (y) to the model 116. As above, the deviation detector 118compares the monitored dependent temperature difference variable (y) tothe value (y_(P)) generated by the model 116 to determine if thedependent temperature difference variable value (y) is significantlydeviating from the predicted temperature difference value (y_(P)).

The AOD system 100 may be in communication with the abnormal situationprevention system 35 (FIGS. 1 and 2A). For example, the AOD system 100may be in communication with the configuration application 38 to permita user to configure the AOD system 100. For instance, one or more of theSPM blocks 112 and 114, the model 116, and the deviation detector 118may have user configurable parameters that may be modified via theconfiguration application 38.

Additionally, the AOD system 100 may provide information to the abnormalsituation prevention system 35 and/or other systems in the processplant. For example, the deviation indicator generated by the deviationdetector 118 or by the status decision block 110 could be provided tothe abnormal situation prevention system 35 and/or the alert/alarmapplication 43 to notify an operator of the abnormal condition. Asanother example, after the model 116 has been trained, parameters of themodel could be provided to the abnormal situation prevention system 35and/or other systems in the process plant so that an operator canexamine the model and/or so that the model parameters can be stored in adatabase. As yet another example, the AOD system 100 may provide (x),(y), and/or (y_(P)) values to the abnormal situation prevention system35 so that an operator can view the values, for instance, when adeviation has been detected.

FIG. 4 is a flow diagram of an example method 150 for detecting anabnormal operation in the hydrocracker or, more particularly, in one ormore of the reactors of the hydrocracker. The method 150 could beimplemented using the example AOD system 100 of FIGS. 3A and 3B and willbe used to explain the operation of the AOD system 100. However, one ofordinary skill in the art will recognize that the method 150 could beimplemented by a system different than the AOD system 100. At a block154, a model, such as the model 116, is trained. For example, the modelcould be trained using the independent load variable X and the dependenttemperature difference variable Y data sets to configure it to model thetemperature difference variable as a function of the load variable. Themodel could include multiple regression models that each model thetemperature difference variable as a function of the load variable for adifferent range of the load variable.

Then, at a block 158, the trained model generates predicted values(y_(P)) of the dependent temperature difference variable Y using values(x) of the independent load variable X that it receives. Next, at ablock 162, the monitored values (y) of the temperature differencevariable are compared to the corresponding predicted values (y_(P)) todetermine if the temperature difference is significantly deviating fromthe predicted temperature difference. For example, the deviationdetector 118 generates or receives the output (y_(P)) of the model 116and compares it to the value (y) of the monitored temperature differencevariable. If it is determined that the monitored temperature differencevariable has significantly deviated from (y_(P)) an indicator of thedeviation may be generated at a block 166. In the AOD system 100, forexample, the deviation detector 118 may generate the indicator. Theindicator may be an alert or alarm, for example, or any other type ofsignal, flag, message, etc., indicating that a significant deviation hasbeen detected (e.g., status=“Up”).

As will be discussed in more detail below, the block 154 may be repeatedafter the model has been initially trained and after it has generatedpredicted values (y_(P)) of the dependent temperature differencevariable Y. For example, the model could be retrained if a set point inthe process has been changed or if a value of the independent loadvariable falls outside of the range x_(MIN), x_(MAX).

Overview of the Model

FIG. 5 is a flow diagram of an example method 200 for initially traininga model such as the model 116 of FIG. 3B. The training of the model 116may be referred to as a LEARNING state, as described further below. At ablock 204, at least an adequate number of data sets (x, y) for theindependent load variable X and the dependent temperature differencevariable Y may be received in order to train a model. As describedabove, the data sets (x, y) may comprise temperature difference and loadvariable data, temperature difference and load variable data that hasbeen filtered or otherwise processed, statistical data generated fromthe temperature difference and load variable data, etc. In the AODsystem of FIGS. 3A and 3B, the model 116 may receive data sets (x, y)from the SPM blocks 112 and 114. Referring now to FIG. 6A, a graph 220shows an example of a plurality of data sets (x, y) received by a model,and illustrating the AOD system in the LEARNING state while the model isbeing initially trained. In particular, the graph 220 of FIG. 6Aincludes a group 222 of data sets that have been collected.

Referring again to FIG. 5, at a block 208, a validity range [x_(MIN),x_(MAX)] for the model may be generated. The validity range may indicatea range of the independent load variable X for which the model is valid.For instance, the validity range may indicate that the model is validonly for load variable X values in which (x) is greater than or equal tox_(MIN) and less than or equal to x_(MAX). As just one example, x_(MIN)could be set as the smallest value of the load variable in the data sets(x, y) received at the block 204, and x_(MAX) could be set as thelargest value of the load variable in the data sets (x, y) received atthe block 204. Referring again to FIG. 6A, x_(MIN) could be set to theload variable value of the leftmost data set, and x_(MAX) could be setas the load variable value of the rightmost data set, for example. Ofcourse, the determination of validity range could be implemented inother ways as well. In the AOD system 100 of FIGS. 3A and 3B, the modelblock 116 could generate the validity range.

At a block 212, a regression model for the range [x_(MIN), x_(MAX)] maybe generated based on the data sets (x, y) received at the block 204. Inan example described further below, after a MONITOR command is issued,or if a maximum number of data sets has been collected, a regressionmodel corresponding to the group 222 of data sets may be generated. Anyof a variety of techniques, including known techniques, may be used togenerate the regression model, and any of a variety of functions couldbe used as the model. For example, the model of could comprise a linearequation, a quadratic equation, a higher order equation, etc. The graph220 of FIG. 6B includes a curve 224 superimposed on the data sets (x, y)received at the block 204 illustrates a regression model correspondingto the group 222 of data sets to model the data sets (x, y). Theregression model corresponding to the curve 224 is valid in the range[x_(MIN), x_(MAX)]. In the AOD system 100 of FIGS. 3A and 3B, the modelblock 116 could generate the regression model for the range [x_(MIN),x_(MAX)]

Utilizing the Model through Operating Region Changes

It may be that, after the model has been initially trained, the systemthat it models may move into a different, but normal operating region.For example, a set point may be changed. FIG. 7 is a flow diagram of anexample method 240 for using a model to determine whether abnormaloperation is occurring, has occurred, or may occur, wherein the modelmay be updated if the modeled process moves into a different operatingregion. The method 240 may be implemented by an AOD system such as theAOD system 100 of FIGS. 3A and 3B. Of course, the method 240 could beimplemented by other types of AOD systems as well. The method 240 may beimplemented after an initial model has been generated. The method 200 ofFIG. 5, for example, could be used to generate the initial model.

At a block 244, a data set (x, y) is received. In the AOD system 100 ofFIGS. 3A and 3B, the model 116 could receive a data set (x, y) from theSPM blocks 112 and 114, for example. Then, at a block 248, it may bedetermined whether the data set (x, y) received at the block 244 is in avalidity range. The validity range may indicate a range in which themodel is valid. In the AOD system 100 of FIGS. 3A and 3B, the model 116could examine the load variable value (x) received at the block 244 todetermine if it is within the validity range [x_(MIN), x_(MAX)]. If itis determined that the data set (x, y) received at the block 244 is inthe validity range, the flow may proceed to a block 252.

At the block 252, a predicted temperature difference variable value(y_(P)) of the dependent temperature difference variable Y may begenerated using the model. In particular, the model generates thepredicted temperature difference variable value (y_(P)) from the loadvariable value (x) received at the block 244. In the AOD system 100 ofFIGS. 3A and 3B, the model 116 generates the predicted temperaturedifference variable value (y_(P)) from the load variable value (x)received from the SPM block 112.

Then, at a block 256, the monitored temperature difference variablevalue (y) received at the block 244 may be compared with the predictedtemperature difference value (y_(P)). The comparison may be implementedin a variety of ways. For example, a difference or a percentagedifference could be generated. Other types of comparisons could be usedas well. Referring now to FIG. 8A, an example received data set isillustrated in the graph 220 as a dot 226, and the correspondingpredicted value, (y_(P)), is illustrated as an “x”. The graph 220 ofFIG. 8A illustrates operation of the AOD system in the MONITORING state.The model generates the prediction (y_(P)) using the regression modelindicated by the curve 224. As illustrated in FIG. 8A, it has beencalculated that the difference between the monitored temperaturedifference variable value (y) received at the block 244 and thepredicted temperature difference value (y_(P)) is −1.754%. Referring nowto FIG. 8B, another example received data set is illustrated in thegraph 220 as a dot 228, and the corresponding predicted temperaturedifference variable value, (y_(P)), is illustrated as an “x”. Asillustrated in FIG. 8B, it has been calculated that the differencebetween the monitored temperature difference variable value (y) receivedat the block 244 and the predicted value (y_(P)) is −19.298%. In the AODsystem 100 of FIGS. 3A and 3B, the deviation detector 118 may performthe comparison.

Referring again to FIG. 7, at a block 260, it may be determined whetherthe monitored temperature difference value (y) received at the block 244significantly deviates from the predicted temperature differencevariable value (y_(P)) based on the comparison of the block 256. Thedetermination at the block 260 may be implemented in a variety of waysand may depend upon how the comparison of the block 256 was implemented.For example, if a temperature difference value was generated at theblock 256, it may be determined whether this difference value exceedssome threshold. The threshold may be a predetermined or configurablevalue. Also, the threshold may be constant or may vary. For example, thethreshold may vary depending upon the value of the independent loadvariable X value received at the block 244. As another example, if apercentage difference value was generated at the block 256, it may bedetermined whether this percentage value exceeds some thresholdpercentage, such as by more than a certain percentage of the predictedtemperature difference variable value (y_(P)). As yet another example, asignificant deviation may be determined only if two or some other numberof consecutive comparisons exceed a threshold. As still another example,a significant deviation may be determined only if the monitoredtemperature difference variable value (y) exceeds the predictedtemperature difference variable value (y_(P)) by more than a certainnumber of standard deviations of the predicted temperature differencevariable value (y_(P)). The standard deviation(s) may be modeled as afunction of the load variable X or calculated from the variable of theresiduals of the training data. A common threshold may be used for eachof the temperature difference variables (ΔT₁-ΔT_(N-1)) being monitored,or different thresholds may be used for some or all of the temperaturedifference variables.

Referring again to FIG. 8A, the difference between the monitoredtemperature difference variable value (y) received at the block 244 andthe predicted temperature difference variable value (y_(P)) is −1.754%.If, for example, a threshold of 10% is to be used to determine whether adeviation is significant, the absolute value of the differenceillustrated in FIG. 8A is below that threshold. Referring again to FIG.8B on the other hand, the difference between the monitored temperaturedifference variable value (y) received at the block 244 and thepredicted temperature difference variable value (y_(P)) is −19.298%. Theabsolute value of the difference illustrated in FIG. 8B is above thethreshold value 10% so an abnormal condition indicator may be generatedas will be discussed below. In the AOD system 100 of FIGS. 3A and 3B,the deviation detector 118 may implement the block 260.

In general, determining if the monitored temperature difference variablevalue (y) significantly deviates from the predicted temperaturedifference variable value (y_(P)) may be implemented using a variety oftechniques, including known techniques. In one implementation,determining if the monitored temperature difference variable value (y)significantly deviates from the predicted temperature difference value(y_(P)) may include analyzing the present values of (y) and (y_(P)). Forexample, the monitored temperature difference variable value (y) couldbe subtracted from the predicted temperature difference variable value(y_(P)), or vice versa, and the result may be compared to a threshold tosee if it exceeds the threshold. It may optionally comprise alsoanalyzing past values of (y) and (y_(P)). Further, it may comprisecomparing (y) or a difference between (y) and (y_(P)) to one or morethresholds. Each of the one or more thresholds may be fixed or maychange. For example, a threshold may change depending on the value ofthe load variable X or some other variable. Different thresholds may beused for different temperature difference variables (ΔT₁, . . . ,ΔT_(1-N)). U.S. patent application Ser. No. 11/492,347, entitled“Methods And Systems For Detecting Deviation Of A Process Variable FromExpected Values,” filed on Jul. 25, 2006, and which was incorporated byreference above, describes example systems and methods for detectingwhether a process variable significantly deviates from an expectedvalue, and any of these systems and methods may optionally be utilized.One of ordinary skill in the art will recognize many other ways ofdetermining if the monitored temperature difference variable value (y)significantly deviates from the predicted temperature differencevariable value (y_(P)). Further, blocks 256 and 260 may be combined.

Some or all of criteria to be used in the comparing (y) to (y_(P))(block 256) and/or the criteria to be used in determining if (y)significantly deviates from (y_(P)) (block 260) may be configurable by auser via the configuration application 38 (FIGS. 1 and 2A) for example.For instance, the type of comparison (e.g., generate difference,generate absolute value of difference, generate percentage difference,etc.) may be configurable. Also, the threshold or thresholds to be usedin determining whether the deviation is significant may be configurableby an operator or by another algorithm. Alternatively, such criteria maynot be readily configurable.

Referring again to FIG. 7, if it is determined that the monitoredtemperature difference variable value (y) received at the block 244 doesnot significantly deviate from the predicted value (y_(P)), the flow mayreturn to the block 244 to receive the next data set (x, y). If however,it is determined that the temperature difference variable value (y) doessignificantly deviate from the predicted value (y_(P)), the flow mayproceed to the block 264. At the block 264, an indicator of a deviationmay be generated. The indicator may be an alert or alarm, for example.The generated indicator may include additional information such aswhether the value (y) received at the block 244 was higher than expectedor lower than expected, for example. Referring to FIG. 8A, because thedifference between the temperature difference variable value (y)received at the block 244 and the predicted value (y_(P)) is −1.754%,which is below the threshold 10%, no indicator is generated. On theother hand, referring to FIG. 8B, the difference between (y) received atthe block 244 and the predicted value (y_(P)) is −19.298%, which isabove the threshold 10%. Therefore, an indicator is generated. In theAOD system 100 of FIGS. 3A and 3B, the deviation detector 118 maygenerate the indicator.

Referring again to the block 248 of FIG. 7, if it is determined that thedata set (x, y) received at the block 244 is not in the validity range,the flow may proceed to a block 268. However, the models developed bythe AOD system are generally valid for the range of data for which themodel was trained. If the load variable X goes outside of the limits forthe model as illustrated by the curve 224, the status is out of range,and the AOD system would be unable to detect the abnormal condition. Forexample, in FIG. 8C, the AOD system receives a data set illustrated as adot 230 that is not within the validity range. This may cause the AODsystem to transition to an OUT OF RANGE state, in which case, the AODsystem may transition again to the LEARNING state, either in response toan operator command or automatically. As such, after the initiallearning period, if the process moves to a different operating region,it remains possible for the AOD system to learn a new model for the newoperating region while keeping the model for the original operatingrange.

Referring now to FIG. 9A, it shows a graph further illustrating receiveddata sets 232 that are not in the validity range when the AOD systemtransitions back to a LEARNING state. In particular, the graph of FIG.9A includes a group 232 of data sets that have been collected. Referringagain to FIG. 7, at the block 268, the data set (x, y) received at theblock 244 may be added to an appropriate group of data sets that may beused to train the model at a subsequent time. Referring to FIG. 9A, thedata set 230 has been added to the group of data sets 232 correspondingto data sets in which the value of X is less than x_(MIN). For example,if the value of the load variable X received at the block 244 is lessthan x_(MIN), the data set (x, y) received at the block 244 may be addedto a data group corresponding to other received data sets in which thevalue of the load variable X is less than x_(MIN). Similarly, if thevalue of the load variable value X received at the block 244 is greaterthan x_(MAX), the data set (x, y) received at the block 244 may be addedto a data group corresponding to other received data sets in which thevalue of the load variable value is greater than x_(MAX). In the AODsystem 100 of FIGS. 3A and 3B, the model block 116 may implement theblock 268.

Then, at a block 272, it may be determined if enough data sets are inthe data group, to which the data set was added at the block 268 inorder to generate a regression model corresponding to the group 232 ofdata sets. This determination may be implemented using a variety oftechniques. For example, the number of data sets in the group may becompared to a minimum number, and if the number of data sets in thegroup is at least this minimum number, it may be determined that thereare enough data sets in order to generate a regression model. Theminimum number may be selected using a variety of techniques, includingtechniques known to those of ordinary skill in the art. If it isdetermined that there are enough data sets in order to generate aregression model, the model may be updated at a block 276, as will bedescribed below with reference to FIG. 10. If it is determined, however,that there are not enough data sets in order to generate a regressionmodel, the flow may return to the block 244 to receive the next data set(x, y). In another example, an operator may cause a MONITOR command tobe issued in order to cause the regression model to be generated.

FIG. 10 is a flow diagram of an example method 276 for updating themodel after it is determined that there are enough data sets in a groupin order to generate a regression model for data sets outside thecurrent validity range [x_(MIN), x_(MAX)]. At a block 304, a range[x′_(MIN), x′_(MAX)] for a new regression model may be determined. Thevalidity range may indicate a range of the independent load variable Xfor which the new regression model will be valid. For instance, thevalidity range may indicate that the model is valid only for loadvariable values (x) in which (x) is greater than or equal to X′MIN andless than or equal to x′_(MAX). As just one example, x′_(MIN) could beset as the smallest value of load variable X in the group of data sets(x, y), and x′_(MAX) could be set as the largest value of load variableX in the group of data sets (x, y). Referring again to FIG. 9A, x′_(MIN)could be set to the load variable value (x) of the leftmost data set inthe group 232, and x′_(MAX) could be set as the load variable value (x)of the rightmost data set in the group 232, for example. In the AODsystem 100 of FIGS. 3A and 3B, the model block 116 could generate thevalidity range.

At a block 308, a regression model for the range [X′_(MIN), x′_(MAX)]may be generated based on the data sets (x, y) in the group. Any of avariety of techniques, including known techniques, may be used togenerate the regression model, and any of a variety of functions couldbe used as the model. For example, the model could comprise a linearequation, a quadratic equation, etc. In FIG. 9B, a curve 234superimposed on the group 232 illustrates a regression model that hasbeen generated to model the data sets in the group 232. The regressionmodel corresponding to the curve 234 is valid in the range [X′_(MIN),x′_(MAX)], and the regression model corresponding to the curve 224 isvalid in the range [x_(MIN), x_(MAX)]. In the AOD system 100 of FIGS. 3Aand 3B, the model 116 could generate the regression model for the range[X′_(MIN), X′_(MAX)].

For ease of explanation, the range [x_(MIN), x_(MAX)] will now bereferred to as [x_(MIN) _(—) ₁, x_(MAX) _(—) ₁], and the range[x′_(MIN), x′_(MAX)] will now be referred to as [x_(MIN) _(—) ₂, x_(MAX)_(—) ₂]. Additionally, the regression model corresponding to the range[x_(MIN) _(—) ₁, x_(MAX) _(—) ₁] will be referred to as f₁(x), andregression model corresponding to the range [x_(MIN) _(—) ₂, x_(MAX)_(—) ₂] will be referred to as f₂(x). Thus, the model may now berepresented as:

$\begin{matrix}{{f(x)} = \left\{ \begin{matrix}{f_{1}(x)} & {{{for}\mspace{14mu} x_{{MIN\_}1}} \leq x \leq x_{{MAX\_}1}} \\{f_{2}(x)} & {{{for}\mspace{14mu} x_{{MIN\_}2}} \leq x \leq x_{{MAX\_}2}}\end{matrix} \right.} & \left( {{Equ}.\mspace{14mu} 1} \right)\end{matrix}$

Referring again to FIG. 10, at a block 316, an interpolation model maybe generated between the regression models corresponding to the ranges[x_(MIN) _(—) ₁, x_(MAX) _(—) ₁] and [x_(MIN) _(—) ₂, x_(MAX) _(—) ₂]for the operating region between the curves 224 and 234. Theinterpolation model described below comprises a linear function, but inother implementations, other types of functions, such as a quadraticfunction, can be used. If x_(MAX) _(—) ₁ is less than x_(MIN) _(—) ₂,then the interpolation model may be calculated as:

$\begin{matrix}{{\left( \frac{{f_{2}\left( x_{{MIN\_}2} \right)} - {f_{1}\left( x_{{MAX\_}1} \right)}}{x_{{MIN\_}2} - x_{{MAX\_}1}} \right)\left( {x - x_{{MIN\_}2}} \right)} + {f_{2}\left( x_{{MIN\_}2} \right)}} & \left( {{Equ}.\mspace{14mu} 2} \right)\end{matrix}$Similarly, if x_(MAX) _(—) ₂ is less than x_(MIN) _(—) ₁, then theinterpolation model may be calculated as:

$\begin{matrix}{{\left( \frac{{f_{1}\left( x_{{MIN\_}1} \right)} - {f_{2}\left( x_{{MAX\_}2} \right)}}{x_{{MIN\_}1} - x_{{MAX\_}2}} \right)\left( {x - x_{{MIN\_}1}} \right)} + {f_{1}\left( x_{{MIN\_}1} \right)}} & \left( {{Equ}.\mspace{14mu} 3} \right)\end{matrix}$

Thus, the model may now be represented as:

$\begin{matrix}{{f(x)} = \left\{ \begin{matrix}{f_{1}(x)} & {{{for}\mspace{14mu} x_{{{MIN}\_}1}} \leq x \leq x_{{{MAX}\_}1}} \\{{\left( \frac{{f_{2}\left( x_{{MIN\_}2} \right)} - {f_{1}\left( x_{{MAX\_}1} \right)}}{x_{{MIN\_}2} - x_{{MAX\_}1}} \right)\left( {x - x_{{MIN\_}2}} \right)} + {f_{2}\left( x_{{MIN\_}2} \right)}} & {{{for}\mspace{14mu} x_{{{MAX}\_}1}} < x < x_{{{MIN}\_}2}} \\{f_{2}(x)} & {{{for}\mspace{14mu} x_{{{MIN}\_}2}} \leq x \leq x_{{{MAX}\_}2}}\end{matrix} \right.} & \left( {{Equ}.\mspace{14mu} 4} \right)\end{matrix}$if x_(MAX) _(—) ₁ is less than x_(MIN) _(—) ₂. And, if x_(MAX) _(—) ₂ isless than x_(MIN) _(—) ₁, the interpolation model may be represented as:

$\begin{matrix}{{f(x)} = \left\{ \begin{matrix}{f_{2}(x)} & {{{for}\mspace{14mu} x_{{{MIN}\_}2}} \leq x \leq x_{{{MAX}\_}2}} \\{{\left( \frac{{f_{1}\left( x_{{MIN\_}1} \right)} - {f_{2}\left( x_{{MAX\_}2} \right)}}{x_{{MIN\_}1} - x_{{MAX\_}2}} \right)\left( {x - x_{{MIN\_}1}} \right)} + {f_{1}\left( x_{{MIN\_}1} \right)}} & {{{for}\mspace{14mu} x_{{{MAX}\_}2}} < x < x_{{{MIN}\_}1}} \\{f_{1}(x)} & {{{for}\mspace{14mu} x_{{{MIN}\_}1}} \leq x \leq x_{{{MAX}\_}1}}\end{matrix} \right.} & \left( {{Equ}.\mspace{14mu} 5} \right)\end{matrix}$

As can be seen from equations 1, 4 and 5, the model may comprise aplurality of regression models. In particular, a first regression model(i.e., f₁(x)) may be used to model the dependent temperature differencevariable Y in a first operating region (i.e., x_(MIN) _(—) ₁≦x≦x_(MAX)_(—) ₁), and a second regression model (i.e., f₂(x)) may be used tomodel the dependent temperature difference variable Y in a secondoperating region (i.e., x_(MIN) _(—) ₂≦x≦x_(MAX) _(—) ₂). Additionally,as can be seen from equations 4 and 5, the model may also comprise aninterpolation model to model the dependent temperature differencevariable Y in between operating regions corresponding to the regressionmodels.

Referring again to FIG. 10, at a block 320, the validity range may beupdated. For example, if x_(MAX) _(—) ₁ is less than x_(MIN) _(—) ₂,then x_(MIN) may be set to x_(MIN) _(—) ₁ and x_(MAX) may be set tox_(MAX) _(—) ₂. Similarly, if x_(MAX) _(—) ₂ is less than x_(MIN) _(—)₁, then x_(MIN) may be set to x_(MIN) _(—) ₂ and x_(MAX) may be set tox_(MAX) _(—) ₁. FIG. 9C illustrates the new model with the new validityrange. Referring to FIGS. 7 and 10, the model may be updated a pluralityof times using a method such as the method 276. As seen from FIG. 9C,the original model is retained for the original operating range, becausethe original model represents the “normal” value for the temperaturedifference variable Y. Otherwise, if the original model were continuallyupdated, there is a possibility that the model would be updated to afaulty condition and an abnormal situation would not be detected. Whenthe process moves into a new operation region, it may be assumed thatthe process is still in a normal condition in order to develop a newmodel, and the new model may be used to detect further abnormalsituations in the system that occur in the new operating region. Assuch, the model for the hydrocracker may be extended indefinitely as theprocess model to different operating regions.

The abnormal situation prevention system 35 (FIGS. 1 and 2A) may cause,for example, graphs similar to some or all of the graphs illustrated inFIGS. 6A, 6B, 8A, 8B, 8C, 9A, 9B, 9C to be displayed on a displaydevice. For instance, if the AOD system 100 provides model criteria datato the abnormal situation prevention system 35 or a database, forexample, the abnormal situation prevention system 35 may use this datato generate a display illustrating how the model 116 is modeling thedependent temperature difference variable Y as a function of theindependent load variable X. For example, the display may include agraph similar to one or more of the graphs of FIGS. 8A, 8B and 9C.Optionally, the AOD system 100 may also provide the abnormal situationprevention system 35 or a database, for example, with some or all of thedata sets used to generate the model 116. In this case, the abnormalsituation prevention system 35 may use this data to generate a displayhaving a graph similar to one or more of the graphs of FIGS. 6A, 6B, 9A,9B. Optionally, the AOD system 100 may also provide the abnormalsituation prevention system 35 or a database, for example, with some orall of the data sets that the AOD system 100 is evaluating during itsmonitoring phase. Additionally, the AOD system 100 may also provide theabnormal situation prevention system 35 or a database, for example, withthe comparison data for some or all of the data sets. In this case, asjust one example, the abnormal situation prevention system 35 may usethis data to generate a display having a graph similar to one or more ofthe graphs of FIGS. 8A and 8B.

Manual Control of AOD System

In the AOD systems described with respect to FIGS. 5, 7 and 10, themodel may automatically update itself when enough data sets have beenobtained in a particular operating region. However, it may be desiredthat such updates do not occur unless a human operator permits it.Additionally, it may be desired to allow a human operator to cause themodel to update even when received data sets are in the validity region.

FIG. 11 is an example state transition diagram 550 corresponding to analternative operation of an AOD system such as the AOD system 100 ofFIGS. 3A and 3B. The operation corresponding to the state diagram 550allows a human operator more control over the AOD system. For example,as will be described in more detail below, an operator may cause a LEARNcommand to be sent to the AOD system when the operator desires that themodel of the AOD system be forced into a LEARNING state 554. Generallyspeaking, in the LEARNING state 554, which will be described in moredetail below, the AOD system obtains data sets for generating aregression model. Similarly, when the operator desires that the AODsystem create a regression model and begin monitoring incoming datasets, the operator may cause a MONITOR command to be sent to the AODsystem. Generally speaking, in response to the MONITOR command, the AODsystem may transition to a MONITORING state 558.

An initial state of the AOD system may be an UNTRAINED state 560, forexample. The AOD system may transition from the UNTRAINED state 560 tothe LEARNING state 554 when a LEARN command is received. If a MONITORcommand is received, the AOD system may remain in the UNTRAINED state560. Optionally, an indication may be displayed on a display device tonotify the operator that the AOD system has not yet been trained.

In an OUT OF RANGE state 562, each received data set may be analyzed todetermine if it is in the validity range. If the received data set isnot in the validity range, the AOD system may remain in the OUT OF RANGEstate 562. If, however, a received data set is within the validityrange, the AOD system may transition to the MONITORING state 558.Additionally, if a LEARN command is received, the AOD system maytransition to the LEARNING state 554.

In the LEARNING state 554, the AOD system may collect data sets so thata regression model may be generated in one or more operating regionscorresponding to the collected data sets. Additionally, the AOD systemoptionally may check to see if a maximum number of data sets has beenreceived. The maximum number may be governed by storage available to theAOD system, for example. Thus, if the maximum number of data sets hasbeen received, this may indicate that the AOD system is, or is in dangerof, running low on available memory for storing data sets, for example.In general, if it is determined that the maximum number of data sets hasbeen received, or if a MONITOR command is received, the model of the AODsystem may be updated and the AOD system may transition to theMONITORING state 558.

FIG. 12 is a flow diagram of an example method 600 of operation in theLEARNING state 554. At a block 604, it may be determined if a MONITORcommand was received. If a MONITOR command was received, the flow mayproceed to a block 608. At the block 608, it may be determined if aminimum number of data sets has been collected in order to generate aregression model. If the minimum number of data sets has not beencollected, the AOD system may remain in the LEARNING state 554.Optionally, an indication may be displayed on a display device to notifythe operator that the AOD system is still in the LEARNING state becausethe minimum number of data sets has not yet been collected.

If, on the other hand, the minimum number of data sets has beencollected, the flow may proceed to a block 612. At the block 612, themodel of the AOD system may be updated as will be described in moredetail with reference to FIG. 13. Next, at a block 616, the AOD systemmay transition to the MONITORING state 558.

If, at the block 604 it has been determined that a MONITOR command wasnot received, the flow may proceed to a block 620, at which a new dataset may be received. Next, at a block 624, the received data set may beadded to an appropriate training group. An appropriate training groupmay be determined based on the load variable value of the data set, forinstance. As an illustrative example, if the load variable value is lessthan x_(MIN) of the model's validity range, the data set could be addedto a first training group. And, if the load variable value is greaterthan x_(MAX) of the model's validity range, the data set could be addedto a second training group.

At a block 628, it may be determined if a maximum number of data setshas been received. If the maximum number has been received, the flow mayproceed to the block 612, and the AOD system will eventually transitionto the MONITORING state 558 as described above. On the other hand, ifthe maximum number has not been received, the AOD system will remain inthe LEARNING state 554. One of ordinary skill in the art will recognizethat the method 600 can be modified in various ways. As just oneexample, if it is determined that the maximum number of data sets hasbeen received at the block 628, the AOD system could merely stop addingdata sets to a training group. Additionally or alternatively, the AODsystem could cause a user to be prompted to give authorization to updatethe model. In this implementation, the model would not be updated, evenif the maximum number of data sets had been obtained, unless a userauthorized the update.

FIG. 13 is a flow diagram of an example method 650 that may be used toimplement the block 612 of FIG. 12. At a block 654, a range [X′_(MIN),x′_(MAX)] may be determined for the regression model to be generatedusing the newly collected data sets. The range [X′_(MIN), x′_(MAX)] maybe implemented using a variety of techniques, including knowntechniques. At a block 658, the regression model corresponding to therange [X′_(MIN), x′_(MAX)] may be generated using some or all of thedata sets collected and added to the training group as described withreference to FIG. 12. The regression model may be generated using avariety of techniques, including known techniques.

At a block 662, it may be determined if this is the initial training ofthe model. As just one example, it may be determined if the validityrange [x_(MIN), x_(MAX)] is some predetermined range that indicates thatthe model has not yet been trained. If it is the initial training of themodel, the flow may proceed to a block 665, at which the validity range[x_(MIN), x_(MAX)] will be set to the range determined at the block 654.

If at the block 662 it is determined that this is not the initialtraining of the model, the flow may proceed to a block 670. At the block670, it may be determined whether the range [X′_(MIN), x′_(MAX)]overlaps with the validity range [x_(MIN), x_(MAX)]. If there isoverlap, the flow may proceed to a block 674, at which the ranges of oneor more other regression models or interpolation models may be updatedin light of the overlap. Optionally, if a range of one of the otherregression models or interpolation models is completely within the range[X′_(MIN), x′_(MAX)], the other regression model or interpolation modelmay be discarded. This may help to conserve memory resources, forexample. At a block 678, the validity range may be updated, if needed.For example, if x′_(MIN) is less than x_(MIN) of the validity range,x_(MIN) of the validity range may be set to the x′_(MIN).

If at the block 670 it is determined whether the range [x′_(MIN),x′_(MAX)] does not overlap with the validity range [x_(MIN), x_(MAX)],the flow may proceed to a block 682. At the block 682, an interpolationmodel may be generated, if needed. At the block 686, the validity rangemay be updated. The blocks 682 and 686 may be implemented in a mannersimilar to that described with respect to blocks 316 and 320 of FIG. 10.

One of ordinary skill in the art will recognize that the method 650 canbe modified in various ways. As just one example, if it is determinedthat the range [x′_(MIN), x′_(MAX)] overlaps with the validity range[x_(MIN), x_(MAX)], one or more of the range [x′_(MIN), x′_(MAX)] andthe operating ranges for the other regression models and interpolationmodels could be modified so that none of these ranges overlap.

FIG. 14 is a flow diagram of an example method 700 of operation in theMONITORING state 558. At a block 704, it may be determined if a LEARNcommand was received. If a LEARN command was received, the flow mayproceed to a block 708. At the block 708, the AOD system may transitionto the LEARNING state 554. If a LEARN command was not received, the flowmay proceed to a block 712.

At the block 712, a data set (x, y) may be received as describedpreviously. Then, at a block 716, it may be determined whether thereceived data set (x, y) is within the validity range [x_(MIN),x_(MAX)]. If the data set is outside of the validity range [x_(MIN),x_(MAX)], the flow may proceed to a block 720, at which the AOD systemmay transition to the OUT OF RANGE state 562. But if it is determined atthe block 716 that the data set is within the validity range [x_(MIN),x_(MAX)], the flow may proceed to blocks 724, 728 and 732. The blocks724, 728 and 732 may be implemented similarly to the blocks 158, 162 and166, respectively, as described with reference to FIG. 4.

To help further explain state transition diagram 550 of FIG. 11, theflow diagram 600 of FIG. 12, the flow diagram 650 of FIG. 13, and theflow diagram 700 of FIG. 14, reference is again made to FIGS. 6A, 6B,8A, 8B, 8C, 9A, 9B, 9C. FIG. 6A shows the graph 220 illustrating the AODsystem in the LEARNING state 554 while its model is being initiallytrained. In particular, the graph 220 of FIG. 6A includes the group 222of data sets that have been collected. After an operator has caused aMONITOR command to be issued, or if a maximum number of data sets hasbeen collected, a regression model corresponding to the group 222 ofdata sets may be generated. The graph 220 of FIG. 6B includes a curve224 indicative of the regression model corresponding to the group 222 ofdata sets. Then, the AOD system may transition to the MONITORING state558.

The graph 220 of FIG. 8A illustrates operation of the AOD system in theMONITORING state 558. In particular, the AOD system receives the dataset 226 that is within the validity range. The model generates aprediction y_(P) (indicated by the “x” in the graph of FIG. 8A) usingthe regression model indicated by the curve 224. In FIG. 8C, the AODsystem receives the data set 230 that is not within the validity range.This may cause the AOD system to transition to the OUT OF RANGE state562.

If the operator subsequently causes a LEARN command to be issued, theAOD system will transition again to the LEARNING state 554. The graph220 of FIG. 9A illustrates operation of the AOD system after it hastransitioned back to the LEARNING state 554. In particular, the graph ofFIG. 9A includes the group 232 of data sets that have been collected.After an operator has caused a MONITOR command to be issued, or if amaximum number of data sets has been collected, a regression modelcorresponding to the group 232 of data sets may be generated. The graph220 of FIG. 9B includes the curve 234 indicative of the regression modelcorresponding to the group 232 of data sets. Next, an interpolationmodel may be generated for the operating region between the curves 224and 234.

Then, the AOD system may transition back to the MONITORING state 558.The graph 220 of FIG. 9C illustrates the AOD system again operating inthe MONITORING state 558. In particular, the AOD system receives thedata set 236 that is within the validity range. The model generates aprediction y_(P) (indicated by the “x” in the graph of FIG. 9C) usingthe regression model indicated by the curve 234 of FIG. 9B.

If the operator again causes a LEARN command to be issued, the AODsystem will again transition to the LEARNING state 554, during which afurther group of data sets are collected. After an operator has caused aMONITOR command to be issued, or if a maximum number of data sets hasbeen collected, a regression model corresponding to the group of datasets may be generated. Ranges of the other regression models may beupdated. For example, the ranges of the regression models correspondingto the curves 224 and 234 may be lengthened or shortened as a result ofadding a regression model between the two. Additionally, theinterpolation model for the operating region between the regressionmodels corresponding to the curves 224 and 234 are overridden by a newregression model corresponding to a curve between curves 224, 234. Thus,the interpolation model may be deleted from a memory associated with theAOD system if desired. After transitioning to the MONITORING state 558,the AOD system may operate as described previously.

One aspect of the AOD system is the user interface routines whichprovide a graphical user interface (GUI) that is integrated with the AODsystem described herein to facilitate a user's interaction with thevarious abnormal situation prevention capabilities provided by the AODsystem. However, before discussing the GUI in greater detail, it shouldbe recognized that the GUI may include one or more software routinesthat are implemented using any suitable programming languages andtechniques. Further, the software routines making up the GUI may bestored and processed within a single processing station or unit, suchas, for example, a workstation, a controller, etc. within the plant 10or, alternatively, the software routines of the GUI may be stored andexecuted in a distributed manner using a plurality of processing unitsthat are communicatively coupled to each other within the AOD system.

Preferably, but not necessarily, the GUI may be implemented using afamiliar graphical windows-based structure and appearance, in which aplurality of interlinked graphical views or pages include one or morepull-down menus that enable a user to navigate through the pages in adesired manner to view and/or retrieve a particular type of information.The features and/or capabilities of the AOD system described above maybe represented, accessed, invoked, etc. through one or morecorresponding pages, views or displays of the GUI. Furthermore, thevarious displays making up the GUI may be interlinked in a logicalmanner to facilitate a user's quick and intuitive navigation through thedisplays to retrieve a particular type of information or to accessand/or invoke a particular capability of the AOD system.

Generally speaking, the GUI described herein provides intuitivegraphical depictions or displays of process control areas, units, loops,devices, etc. Each of these graphical displays may include statusinformation and indications (some or all of which may be generated bythe AOD system described above) that are associated with a particularview being displayed by the GUI. A user may use the indications shownwithin any view, page or display to quickly assess whether a problemexists within the hydrocracker or a reactor of the hydrocracker depictedwithin that display.

Additionally, the GUI may provide messages to the user in connectionwith a problem, such as an abnormal situation, that has occurred orwhich may be about to occur within the hydrocracker. These messages mayinclude graphical and/or textual information that describes the problem,suggests possible changes to the system which may be implemented toalleviate a current problem or which may be implemented to avoid apotential problem, describes courses of action that may be pursued tocorrect or to avoid a problem, etc.

FIGS. 15-19 are exemplary depictions of displays that may be generatedfor the AOD system and displayed to an operator. In the example shown, arepresentation 600 of a reactor of the hydrocracker is displayed, suchas one of the reactors 64, 66 shown in FIG. 2B. Of course, it should beunderstood that multiple reactors of the hydrocracker may be displayed.The reactor being monitored has four cross-sections or “beds” across thereactor at which a cross-sectional temperatures are measured, though thenumber of cross-sections or “beds” may vary depending on the reactor.The temperature for each cross-section, such as the average bedtemperature (WABT₁-WABT₄), is displayed for each cross-section. As notedabove with reference to FIG. 2B, the WABT for each cross-section may beprovided as a weighted average of multiple temperature measurementsT_(i) that are measured at the cross-section.

The temperature difference (ΔT₁-ΔT₃) between each pair of WABT's iscalculated and displayed. In one implementation, the representationsdepicting the ΔT's may be automatically adjusted by the AOD system toreflect the number of number of ΔT's being monitored. As shown in FIG.15, a button 602 is associated with each ΔT, which, if selected, causesa faceplate 604 to be generated in response thereto. It should beunderstood that although a button is depicted, other methods ofselection may be utilized such as radio buttons, dropdown menus or othergraphical links, or non-graphical methods of selection (e.g., keyboard,voice, etc.) may be utilized to generate the faceplate 604 associatedwith a particular temperature difference variable ΔT or to execute anyof a number of options presented by the displays.

The faceplate 604 include a variety of information, including, but notlimited to, mode, status, current monitored ΔT (y), predicted ΔT(y_(P)), current regression model, quality of regression fit, etc. Thefaceplate may further provide the ability to control user-configurableparameters, such as learning length, statistical calculation length,regression order and threshold limits, for example. Theuser-configurable parameters may be controlled by data entry, graphicalmanipulation, or any of a number of configuration methods.

When an abnormal situation is detected, such as a significant increasein any of the ΔT's indicating a temperature runaway condition, an alertor other indication may be generated. Referring now to FIG. 16, if amonitored value (y) of the temperature difference variable ΔT₁significantly deviates from the predicted value (y_(P)) of thetemperature difference variable ΔT₁, the representation of thetemperature difference variable associated with the abnormal conditionis highlighted 606 and an alarm banner at the bottom of the displayshows the alarm 608. Of course, other methods of displaying an alert maybe utilized.

The user may request further information relating to the abnormalcondition indication, for example by selecting the alarm 608 in thealarm banner or selecting the highlighted temperature differencevariable 606. Referring now to FIG. 17, a summary message 610 relatingto the abnormal condition is generated in response to the request forfurther information. The summary message provides at least some furtherinformation about the abnormal condition, such as the monitoredtemperature difference variable value (y) (shown as “current ΔT: 54.1°F.”) and the predicted temperature difference variable value (y_(P))(shown as “expected ΔT: 41.2° F.”).

As seen in FIG. 17, the summary message 610 provides a graphical linkfor further, more detailed information relating to the abnormalcondition. Referring now to FIG. 18, a display window is generateddepicting more detailed information relating to the abnormal condition.The more detailed information may include, but is not limited to, guidedassistance to address the abnormal condition, such as instructions tosolve the abnormal condition (e.g., temperature adjustment, shutdown,replacement parts order, work order, etc.).

FIG. 19 is an exemplary display that may be generated to allow anoperator to configure and view the AOD system. For example, manipulationof the AOD system may be implemented as a control module, which may haveone or more function blocks, on a variety of process control platforms,including, but not limited to the DeltaV™ and Ovation control systems,sold by Emerson Process Management. In other implementation,manipulation of the AOD system may be implemented as a field deviceinterface module, such as the Rosemount 3420 sold by Emerson ProcessManagement. In yet another implementation, manipulation of the AODsystem may be implemented as a stand-alone algorithm. In the example ofFIG. 19, manipulation of the AOD system is illustrated as an abnormalsituation prevention module implemented in DeltaV™, and allows anoperator to configure the AOD system. For example, the operator maycontrol the LEARN and MONITOR phases of the AOD system, specify theWABT's to include, specify the statistical blocks, if any, to utilize,specify automatic or manual modes of operation, vary the learninglength, vary the statistical calculation period, enable relearning fordifferent operating regions, specify deviation thresholds, or any of anumber of configurable options associated with the AOD system.

Although examples were described in which a regression model comprised alinear regression model of a single dependent variable as a function ofa single independent variable, one of ordinary skill in the art willrecognize that other linear regression models and non-linear regressionmodels may be utilized. One of ordinary skill in the art will alsorecognize that the linear or non-linear regression models may modelmultiple dependent variables as functions of multiple independentvariables.

The AOD systems, models, regression models, interpolation models,deviation detectors, logic blocks, method blocks, etc., described hereinmay be implemented using any combination of hardware, firmware, andsoftware. Thus, systems and techniques described herein may beimplemented in a standard multi-purpose processor or using specificallydesigned hardware or firmware as desired. When implemented in software,the software may be stored in any computer readable memory such as on amagnetic disk, a laser disk, or other storage medium, in a RAM or ROM orflash memory of a computer, processor, I/O device, field device,interface device, etc. Likewise, the software may be delivered to a useror a process control system via any known or desired delivery methodincluding, for example, on a computer readable disk or othertransportable computer storage mechanism or via communication media.Communication media typically embodies computer readable instructions,data structures, program modules or other data in a modulated datasignal such as a carrier wave or other transport mechanism. The term“modulated data signal” means a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia includes wired media such as a wired network or direct-wiredconnection, and wireless media such as acoustic, radio frequency,infrared and other wireless media. Thus, the software may be deliveredto a user or a process control system via a communication channel suchas a telephone line, the Internet, etc. (which are viewed as being thesame as or interchangeable with providing such software via atransportable storage medium).

Thus, while the present invention has been described with reference tospecific examples, which are intended to be illustrative only and not tobe limiting of the invention, it will be apparent to those of ordinaryskill in the art that changes, additions or deletions may be made to thedisclosed embodiments without departing from the spirit and scope of theinvention.

1. A computer implanted method of facilitating detection of an abnormaloperation of a hydrocracker, using a computer having a non-transitorycomputer-readable storage medium having a computer executableinstructions stored in the non-transitory computer-readable storagemedium for implementing the method, the method comprising: collectingfirst data sets generated from a temperature difference variable betweena first cross section in a reactor of the hydrocracker and a secondcross section in the reactor of the hydrocracker while the hydrocrackeris in a first operating region and generated from a load variable of thehydrocracker while the hydrocracker is in the first operating region;generating a first regression model of the process in the firstoperating region using the first data sets; determining a first range inwhich the first regression model is valid; generating a model of thehydrocracker to include the first regression model; collecting seconddata sets generated from the temperature difference variable between thefirst cross section in a reactor of the hydrocracker and the secondcross section in the reactor of the hydrocracker while the hydrocrackeris in a second operating region and generated from a load variable ofthe hydrocracker while the hydrocracker is in the second operatingregion; generating a second regression model of the hydrocracker in thesecond operating region using the second data sets; determining a secondrange in which the second regression model is valid; and revising themodel of the hydrocracker to include the first regression model for thefirst range and the second regression model for the second range.
 2. Themethod of claim 1, further comprising: prior to collecting the seconddata sets, receiving a data set outside of the first range; andgenerating an indicator that the received data set is outside of a validrange of the hydrocracker model; wherein collecting the second data setscomprises collecting the second data sets in response to receiving anauthorization.
 3. The method of claim 2, wherein the authorizationcomprises an authorization to train the model for the second operatingregion.
 4. The method of claim 3, wherein the authorization comprises anauthorization to begin monitoring the hydrocracker; wherein generatingthe second regression model comprises generating the second regressionmodel in response to receiving the authorization to begin monitoring thehydrocracker after receiving authorization to train the model.
 5. Themethod of claim 2, further comprising revising the model in response toreceiving the authorization.
 6. The method of claim 2, wherein theauthorization comprises a user authorization.
 7. A system for detectingan abnormal operation in a hydrocracker in a process plant, comprising:a configurable model of the hydrocracker in the process plant, theconfigurable model including a first regression model in a first rangecorresponding to a first operating region of the hydrocracker, theconfigurable model capable of being subsequently configured to include asecond regression model in a second range corresponding to a secondoperating region different than the first operating region, wherein theconfigurable model is capable of generating a prediction of atemperature difference variable value as a function of a load variablevalue, wherein the temperature difference variable value is generatedfrom a temperature difference between a first cross section in a reactorof the hydrocracker and a second cross section in the reactor of thehydrocracker; and a deviation detector coupled to the configurablemodel, the deviation detector configured to determine if the temperaturedifference variable value differs from the predicted temperaturedifference variable value by comparing a difference between thetemperature difference variable value and predicted temperaturedifference variable value to a threshold.
 8. The system of claim 7wherein, after the configuration model is configured to include thesecond regression model, the configurable model is capable of:generating the prediction of the temperature difference variable valueusing the first regression model if the load variable value is in thefirst range, generating the prediction of the temperature differencevariable value using the second regression model if the load variablevalue is in the second range.
 9. A system for detecting an abnormaloperation in a hydrocracker in a process plant, comprising: aconfigurable model of the hydrocracker in the process plant, theconfigurable model including a plurality of regression models regressionmodel in a range corresponding to an operating region of thehydrocracker, wherein the configurable model is capable of generating aplurality of predictions of temperature difference variable values eachas a function of a load variable value, wherein each temperaturedifference variable value is generated from a corresponding temperaturedifference between cross sections in a reactor of the hydrocracker; adeviation detector coupled to the configurable model, the deviationdetector configured to determine if at least one of the temperaturedifference variable values differs from the corresponding predictedtemperature difference variable value by comparing a difference betweenthe at least one of the temperature difference variable values and thecorresponding predicted temperature difference variable value to athreshold; and an integration application stored on a non-transitorycomputer readable memory and adapted to be executed on a processor tocreate a representation of the hydrocracker for use in viewing thetemperature difference variable value and for use in viewing anindication of an abnormal operation of the hydrocracker in response tocomparing the difference between the temperature difference variablevalue and the predicted temperature difference variable value to athreshold, and wherein the integration application is configured tocreate an indication of an abnormal operation of the hydrocracker if adifference between at least one of the temperature difference variablevalues and the corresponding predicted temperature difference variablevalue exceeds the threshold and generates an identification of thetemperature difference variable causing the abnormal operation.